Dreams of Futures Past
I Dreamed a Dream of a Time Yet Seen
Had an interesting idea about a device that might be useful in a post-agi society, which to me naturally lends towards a transitioning to post-labor society. In such a space i think people's thoughts and ideas are where the value is in making better and better ai models. In order to do so at a scale high enough to be of use in that time though, the means of collection must be incentivized as essential in opinion of daily people. Something so useful why wouldn't they have one, similar to how the cellphone is treated in 2025.
Given how popular markdown is for working with ai training data and ai models, and how popular obsidian.md is among people and companies, seems natural to me providing a secure device that can be used to record and transform data in novel and valuable ways as well as serve as a means of interfacing that data with the data ecosystem/data-economy.
Apps could be written for it to allow it to run a mesh network within range of it's wifi card. This could be used to allow it to serve as a hub for multiple devices within range being at the user's control/interactivity range.
Why is this better than a thumb drive?
Well, the cpu and ram and storage capacity are greater, allowing for expanded functionality. It could even distribute computing across devices it's connected to for greater performance. It should come with at least one AI model optimized to reason and function as a personal assistant agent. This will keep it entirely local to the user preventing data-snooping as long as not running insecure distributed computation links.
So i asked chatGPT to do Deep Research on the idea.
Check it out:
o3-mini-high+Deep Research
You said:
I'm imagining a device that can be worn on someone's person easily, that carries bluetooth and nfc and wifi capability at the minimum, as well as solid state drives. This device can pair with any tablet, phone, ar/vr headset/smart glasses/smart glass display, etc. It's function is to serve as a personal knowledge database cloud network that travels with them at all times. It is local for maximum efficiency and security. It could even serve as a component of a proof of life digital security fob. Ideally it can be upgraded by the user however they choose, as long as it's baseline purpose is an easy to carry Personal Area Network device for extending our minds and ability to interact with our environments. Let's take a look at components available on the market today for a possible prototype device along these lines, as well as what open-source software exists to help this goal and what would need to be built to make the prototype usable as described. I imagine a user might store a small (4b or smaller size LLM) ai model/s on drives to assist in their daily tasks, perhaps even agent/s to interact with other ai systems for the user reducing friction between user desire and fulfilment. Ideally the device gets small enough to fit inside a belt or strap construct that can be worn without being obtrusive to the user given enough time. But a prototype could be larger and less comfortable for the sake of developing proof of concept. I think power supply, efficiency, and capacity/capabilities are the metrics to build for with cost effectiveness and duration of use alongside ease of use as the guiding principles for a prototype. I live in the US and will be building this here so lets do our diligence for available build component options research. If we made it a helmet type item, we could have a smart visor type of setup similar to how apple and meta's vr devices work using a camera to show the user what's happening on the other side of the vr glasses. We could also add an eeg sensor setup to the helmet for expanded function options from monitoring brainwaves to biofeedback controls. I think one of the largest values humans can provide in a post-agi society/world is the data they create with original thoughts and creations. I think a device like this would be an evolved thumbdrive/phone that allows storage of a personal knowledge database such as an obsidian vault as well as serving as a personal media server and hosting a personal ai assistant/s. Such a device would be ideal for collecting and optimizing the value add capacity the user can gain from licensing/sharing their data. I could also see a vest architecture working, or a forearm bracer structure. Help me research all my options for bringing this idea to life as well as any considerations I should make before pursuing it.
ChatGPT said:
This is a fascinating and ambitious project! To ensure I provide the most useful and targeted research, could you clarify the following details:
- Form Factor Focus: Are you leaning more towards a helmet, vest, belt, or bracer for the first prototype, or should I explore all possibilities equally?
- Primary Use Cases: While you outlined several functionalities, which ones are the highest priority for an MVP (e.g., local AI assistant, personal media server, proof-of-life security, EEG sensing, etc.)?
- Budget Range: Should I focus on the most cutting-edge components available or balance cost-effectiveness for a reasonable prototype budget?
- Preferred Open-Source Stack: Do you have any specific OS, frameworks, or software in mind, or should I explore all viable open-source solutions?
- Upgradeability: Are there particular modular upgrade paths you want prioritized (e.g., storage, processing power, connectivity)?
- Power Considerations: Should the prototype prioritize battery life, energy efficiency, or wired power options?
Once you provide some guidance on these aspects, Iāll conduct in-depth research on available components, open-source software, and practical considerations to bring this idea to life!
You said:
- Form Factor Focus: i'd like you to explore all possibilities, including providing an analysis of best options for prototype based on the guiding principles.
- Primary use cases: Personal media server/local ai assistant. The rest can be added on from here. We can look at the Omi2 personal ai medallion for inspiration on the ai functionality/hardware options.
- Budget Range: Show minimum cost options and cutting edge options, and a set of min-max recommendations to optimize our budget in 2 budget sizes low-middle and high.
- Preferred open-source stack: explore all viable solutions upgradability: storage, processing power, power duration/recharge time, connectivity. in that order of priority.
- Power considerations: wired power options should be only a last resort for the prototype and gone in the production model v1. Energy efficiency first, serial batteries or alternative battery options second.
ChatGPT said:
Great! Iāll conduct an in-depth analysis of available components and open-source solutions for a wearable Personal Area Network device, considering form factor, primary use cases, budget constraints, and power efficiency. I'll also explore prototype and production-ready options for local AI assistance and media server functionality while evaluating best practices for upgradeability.
Iāll get back to you soon with a detailed report on component selection, open-source software options, power solutions, and an analysis of the best form factors for a prototype.
Researching Wearable Personal Area Network Device
ChatGPT
Iām examining wearable Personal Area Network devices, focusing on form factors, hardware components, power solutions, open-source software, budget analysis, upgradeability, and industry comparisons.
Analyzed with python
Searched for Omi2 Personal AI Medallion wearable
ChatGPT
Iām headed towards a Designboom article to assess its relevance to Omi and other results.
Analyzed with python
ReadĀ designboom.com
ChatGPT
Iām gathering info about Omi's design, especially its brain interface and smartphone connectivity. Also noting its attachment method and listening capabilities.
Analyzed with python
ReadĀ ainews.com
ChatGPT
The Omi device boasts a price of $89, offers up to 3 days of battery life, and integrates with a smartphone app. It includes 250 open-source apps and AI Personas.
Analyzed with python
ReadĀ wired.com
ChatGPT
Iām thinking about the form factors for wearable AIsābelt, vest, bracer, and helmet. Iāll weigh the pros and cons of each type, focusing on the user experience.
Analyzed with python
Searched for wearable computer form factors
ChatGPT
Interestingly enough, wearable computers come in various forms, such as vests and belts, each offering unique advantages. Exploring these alternatives boosts their practicality and potential applications.
Analyzed with python
Searched for wearable computing belt or vest
ChatGPT
Unlocking insights on wearable tech form factors from the article.
Analyzed with python
ReadĀ medium.com
ChatGPT
Iām gathering info on a Wikipedia page for wearable computers. This makes me think more about wearable technology dynamics.
Analyzed with python
ReadĀ en.wikipedia.org
ChatGPT
Interestingly enough, the text outlines typical placements like wrist and neck, rather than addressing belt vs vest specifically. Checking OpenAI's policies.
Analyzed with python
ReadĀ geocities.ws
ChatGPT
Examining portable prototypes like vests, belts, or bracers, considering efficiency, cost, comfort, and ease of use. This journey leads to intriguing ideas.
Analyzed with python
Searched for Raspberry Pi wearable computer belt
ChatGPT
Iām gathering info on wearable computers and Google Glass-style devices, diving into instructables, Circuit Digest, and Hackster for guidance.
Analyzed with python
ReadĀ instructables.com
ChatGPT
Piecing together a Raspberry Pi Model B setup with a rechargeable AA pack, Vuzix glasses, and WiFi adapter, likely worn attuned to a belt.
Analyzed with python
Read more fromĀ instructables.com
ChatGPT
Comparing SBCs like Raspberry Pi 4, Jetson Nano, and NUC for AI and media server capabilities.
Analyzed with python
Searched for Raspberry Pi vs Jetson wearable AI processing
ChatGPT
Considering Raspberry Pi 5 with its notable NPU and GPU advancements, and contrasting it with alternatives like Udoo Bolt, LattePanda, and Jetson for edge AI applications.
Analyzed with python
Searched for wearable personal media server
ChatGPT
Iām looking at open-source media server software like Jellyfin and Plex for streaming to AR glasses or headphones. Considering options for portability and feasibility.
Analyzed with python
Searched for open source offline AI assistant hardware
ChatGPT
Iām pulling together info on open source AI projects like Yarn, OwlAI, and Mattcool, noting their significance and potential alignment with the current concept.
Analyzed with python
ReadĀ github.com
Analyzed with python
Read more fromĀ github.com
ChatGPT
I'm examining form factors like belt, vest, bracer, and helmet for a wearable prototype. Each has distinct advantages and drawbacks, including comfort, ease, and portability.
Analyzed with python
ReadĀ hackster.io
ChatGPT
Iām examining Freedman's AR glasses, using a RasPi on a belt. Options include belt, vest, bracer, or helmet. Choosing depends on device size and requirements. Progressing through connectivity and power efficiency.
Analyzed with python
Searched for low power AI accelerators for wearables
ChatGPT
Discussing file search outcomes and implications.
Analyzed with python
Searched for low power AI accelerators for wearables
ChatGPT
Iām gathering info on various systems, like Jetson Nano and Raspberry Pi. Jetson Nano's 472 GFLOPS FP16 stands out, while RPi's reliance on Coral for heavy tasks is noteworthy.
Analyzed with python
Searched for NVIDIA Jetson Nano performance and power consumption
ChatGPT
I'm gathering info on Xavier NX and Orin Nano specs, considering power solutions like batteries and converters, and evaluating various SBCs for a balanced cost/performance ratio.
I'm gathering info on Raspberry Pi 5 and Jetson Nano for mid-range options, considering upgradeability through modular design and standardized interfaces like M.2 slots and external ports.
Analyzed with python
Searched for Humane AI Pin wearable device
ChatGPT
Humane AI Pin features a chest-worn device with camera, projector, and microphone. Subscription-based, it offers AI assistance and faces critiques like limited offline functionality and high cost.
Analyzed with python
ReadĀ wired.com
ChatGPT
The discussion touches on connectivity, temperature control, battery life, hardware inclusion, design focus, and user interface.
Analyzed with python
Read more fromĀ wired.com
ChatGPT
Let's find more about wearable vest computers and forearm computers. "SCOTTeVEST" and "Pip-Boy" come to mind. Also considering military rugged devices.
Analyzed with python
Analyzed with python
ChatGPT
Iām tracking how the search engine might be struggling, as no results appeared after 22 minutes of analysis.
Analyzed with python
Searched for forearm wearable PC device
ChatGPT
Iām imagining rugged devices like Honeywell CW45 worn on the arm, similar to a smartphone. Considering quick access via display or interface wearables like glasses or phones.
Analyzed with python
Searched for graphene battery wearable devices energy density
ChatGPT
I'm exploring graphene's high energy density, reaching 600 Wh/kg. This surpasses conventional Li-ion batteries, though the reliability of this information requires further verification.
Analyzed with python
ReadĀ grepow.com
Analyzed with python
Read more fromĀ grepow.com
ChatGPT
I see the site highlights graphene batteries' superior energy density and power storage. Additionally, solid-state batteries offer safety and potential density improvements, though they are still emerging.
Analyzed with python
Searched for solid-state battery wearable advantages
Analyzed with python
ReadĀ en.wikipedia.org
Analyzed with python
Read more fromĀ en.wikipedia.org
ChatGPT
Iām noting the advantages and applications of graphene, solid-state, and Li-ion batteries, including brands and customization for wearable and NUC.
Analyzed with python
Searched for open source wearable battery pack design
ChatGPT
: I'm evaluating the Omi AI site for tips on low power design, especially if it's specific to Omi devices.
Analyzed with python
ReadĀ omi.me
ChatGPT
Transferring_Callback
Analyzed with python
Searched for Raspberry Pi running LLaMA GPT offline
ChatGPT
Summarizing mediums and YT segments about LLaMA on Pi 4 or 5.
Analyzed with python
ReadĀ reddit.com
ChatGPT
I'm piecing together images like wearable PCs, old belt PCs, computing vests, Jetson Nano boards, and conceptual wearable tech for integration.
Analyzed with python
ReadĀ geocities.ws
ChatGPT
OK, let's see. A thumbnail shows an instructable image, which might feature a person or just the device. The step mentions a cell phone case and a battery in a pocket, but it's not clear if a person is involved.
Analyzed with python
ReadĀ images.ctfassets.net
ChatGPT
I found a contest banner featuring a person. This could impact my project, so I'll examine how it might be used!
Analyzed with python
ReadĀ content.instructables.com
ChatGPT
OK, let me see. Iām checking image policies to ensure no real people's identities are revealed. Considering using image with a caption or mentioning in the text. Alternatively, focusing on the device or a generic concept might be better.
Analyzed with python
ReadĀ instructables.com
ChatGPT
Considering "cyberdeck wearable images" for inspiration or using a conceptual image of wearable tech from Clint Zeagler's "body maps".
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ReadĀ clintzeagler.com
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Read more fromĀ clintzeagler.com
ChatGPT
Iām detailing the advantages of weight distribution in wearable tech, recommending belts or vests for heavy parts like batteries, and providing examples of industry comparisons and past attempts.
Analyzed with python
Searched for Magic Leap 2 compute pack battery life
ChatGPT
Iām laying out sections for an AR wearable, covering form factors, hardware, power solutions, open-source software, budget tiers, upgradeability, industry inspiration, and concluding recommendations.
Analyzed with python
ReadĀ blog.rareschool.com
ChatGPT
Comparing Jetson Nano, Pi4, and NUC for training tasks, considering power usage and GPU capacity.
Analyzed with python
Searched for Raspberry Pi 5 specs and NVMe performance
ChatGPT
I'm wondering about the Pi 5's improved CPU and its PCIe for NVMe, which contrasts with Pi 4's USB, emphasizing modular NVMe solutions.
Analyzed with python
ReadĀ forums.raspberrypi.com
ChatGPT
I'm comparing the Pi 5's PCIe x1 interface for NVMe speeds to USB 3 on Pi4, considering the form factor for heavy loads, and noting the RPi vs Jetson performance differences.
ReadĀ wolfpaulus.com
ChatGPT
I'm recommending a brief note on the Pi5's superior CPU, noting its speed and architectural upgrade, and contrasting its internal NVMe storage with external options.
Analyzed with python
ReadĀ tomshardware.com
ChatGPT
I'm considering the use of 5G and UWB for enhancing connectivity and precise location tracking. Also, examining the battery life of the Omi device.
Analyzed with python
ReadĀ ainews.com
ChatGPT
Iām pulling in references from Omi and Bee, highlighting battery life and small form factors with bullet lists and citations.
Analyzed with python
ReadĀ github.com
ChatGPT
OK, let's think this through: the image might be a blocked link or found on Omi designboom.
Analyzed with python
ReadĀ static.designboom.com
ChatGPT
Taking a closer look at the physical product and model image for inclusion. Next, I'll assess the Raspberry Pi and Jetson images to finalize the decision.
Analyzed with python
Searched for Raspberry Pi 4 Model B image
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Searched for Jetson Nano board image
ChatGPT
Evaluating images for a wearable PC rig, possibly excluding boards or other imagery. The Freedman article might feature a banner with someone wearing glasses.
Analyzed with python
Searched for Magic Leap 2 compute pack image
ChatGPT
I'm looking into whether to include general battery or board photos from sources like Wikipedia, which might be suitable for enhancing engagement.
Analyzed with python
ReadĀ en.wikipedia.org
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Searched for Jetson Nano jpg image
ChatGPT
Hmm, it seems like user_files aren't needed. This might shift our strategy elsewhere.
Analyzed with python
Searched for Jetson Nano 128-core Maxwell GPU
ChatGPT
Piecing together the technical details in the text to ensure accurate citations.
Analyzed with python
Searched for modular wearable computer concept image
ChatGPT
Piecing together deliberate and comprehensive text.
Analyzed with python
Research completed in 7mĀ Ā· 33 sources
Citations
[
Where It ā BODY MAPS ā Clint Zeagler
- Heavy items should not be placed on the bodyās extremities for long periods of time. * Batteries for wearable device tend to be the source of most of the weight. If a device needs a large battery (to last a long time or because it needs large amounts of power to function) place the large battery on the waist. If the wearable needs to be located on a different part of the body for use then consider distributing the power from the area of use. Finally consider distributing battery cells instead of using one large battery. * From a design perspective weight also has a visceral quality. Density, or
](https://www.clintzeagler.com/where-it-body-maps/#:~:text=,Density%2C or)[
Where It ā BODY MAPS ā Clint Zeagler
- The lower waist is a good area for heavy loads. * Weight should be balanced across the body evenly, and aligned to the center of gravity is possible. * Heavy items should not be placed on the bodyās extremities for long periods
](https://www.clintzeagler.com/where-it-body-maps/#:~:text=,bodyās extremities for long periods)[
Wearable Computing
Image A different form factor was used by Via: the computer was in the shape of a belt and could truly be worn. Both the Xybernaut and the Via proved to be very useful devices in our prototype developments and experiments involving wearable computing systems and the interface technologies associated with wearable computing.
RaspberryPi Powered Wearable Computer : 8 Steps (with Pictures) - Instructables
match at L226 wear.Ā I got a cell phone case that clips onto my belt, so the first thing I do is put it on my belt.Ā Then, I put the battery pack in my pocket, and connect the microUSB cable to the Pi.Ā The extra bits of the microUSB cable can be shoved into your pocket.Ā I put the video glasses control box in a separate
RaspberryPi Powered Wearable Computer : 8 Steps (with Pictures) - Instructables
wear.Ā I got a cell phone case that clips onto my belt, so the first thing I do is put it on my belt.Ā Then, I put the battery pack in my pocket, and connect the microUSB cable to the Pi.Ā The extra bits of the microUSB cable can be shoved into your pocket.Ā I put the video glasses control box in a separate
Where It ā BODY MAPS ā Clint Zeagler
- Weight, Load, or the pressure of weight should be placed on the fleshy, but non-sensitive parts of the body, avoiding boney areas. * The lower waist is a good area for heavy loads. * Weight should be balanced across the body evenly, and aligned to the center of gravity is possible. * Heavy items should not be placed on the bodyās extremities for long periods of time. * Batteries for wearable device tend to be the source of most of the weight. If a device needs a large battery (to last a long time or because it needs large amounts of power to function) place the large battery on the waist. If the
](https://www.clintzeagler.com/where-it-body-maps/#:~:text=,If the)[
Where It ā BODY MAPS ā Clint Zeagler
- Heavy items should not be placed on the bodyās extremities for long periods of time. * Batteries for wearable device tend to be the source of most of the weight. If a device needs a large battery (to last a long time or because it needs large amounts of power to function) place the large battery on the waist. If the wearable needs to be located on a different part of the body for use then consider distributing the power from the area of use. Finally consider distributing battery cells instead of using one large battery.
](https://www.clintzeagler.com/where-it-body-maps/#:~:text=,of using one large battery)[
Dragonfly Wearable Computer - AML
Dragonfly Wearable Computer - AML AML's Dragonfly wearable computer is a rugged, high-performance device that gives users immediate access to their data, while not impeding their speed and ...
Where It ā BODY MAPS ā Clint Zeagler
of gravity is possible. * Heavy items should not be placed on the bodyās extremities for long periods of time. * Batteries for wearable device tend to be the source of most of the weight. If a device needs a large battery (to last a long time or because it needs large amounts of power to function) place the large battery on the waist. If the wearable needs to be located on a different part of the body for use then consider distributing the power from the area of use. Finally consider distributing battery cells instead of using one large battery.
Raspberry Pi 5 with NVMe - Wolf Paulus
Raspberry Pi 5 with NVMe - Wolf Paulus It features a significantly upgraded ARM Cortex-A76 processor clocked at 2.4GHz, a noticeable improvement over the Cortex-A72 at 1.5GHz found in the Raspberry ...
](https://wolfpaulus.com/rp5#:~:text=Raspberry Pi 5 with NVMe,found in the Raspberry)[
Jetson NANO 4GB B01(Official/SUB) Developer Kit - Yahboom
Jetson NANO 4GB B01(Official/SUB) Developer Kit - Yahboom The JETSON NANO with a quad-core CORTEX-A57 processor, a 128-core MAXWELL GPU and 4GB of LPDDR memory with power Al computing power to provide 472GFLOP and support a range of popular AI frameworks and algorithms such as TENSORFLOW, PYTORCH, CAFFE /CAFFE2, KERAS, MXNET, etc.
Training ANNs on the Raspberry Pi 4 and Jetson Nano
Conclusion
](https://blog.rareschool.com/2019/07/training-anns-on-raspberry-pi-4-and.html#:~:text=)[
NVIDIA® Jetson Xavier⢠NX Module 16 GB - Seeed Studio
NVIDIA® Jetson Xavier⢠NX Module 16 GB - Seeed Studio Jetson Xavier NX 16GB delivers up to 21 TOPS for running modern AI workloads, consumes as little as 10 watts of power, and has a compact form factor smaller ...
Pimoroni NVMe Base for Raspberry Pi 5 Review: NVMe for Me | Tom's Hardware
Header Cell - Column 0 Pimoroni NVMe Base Hat Drive Bottom (2280) PCIe Gen 1 Read 235 MB/s 235 MB/s PCIe Gen 1 Write 194.5 MB/s 194.5 MB/s PCIe Gen 2 Read 416 MB/s 469 MB/s PCIe Gen 2 Write 380.9 MB/s 383.2 MB/s PCIe Gen 3 Read 876 MB/s 863 MB/s PCIe Gen 3 Write 761.9 MB/s 719.1 MB/s Micro SD Read 90.5 MB/s Row 6 - Cell 2 Micro SD Write 30.8 MB/s Row 7 - Cell 2
4-way NVMe RAID comes to Raspberry Pi 5 | Jeff Geerling
4-way NVMe RAID comes to Raspberry Pi 5 | Jeff Geerling Today's board is the Geekwork X1011, which puts four NVMe SSDs under a Raspberry Pi. Inland 256GB NVMe SSDs installed on X1011 on Raspberry Pi 5.
Raspberry Pi 5 with 52Pi NVMe hat, Samsung 990 EVO SSD
Raspberry Pi 5 with 52Pi NVMe hat, Samsung 990 EVO SSD The SSD reaches a read speed of ~444MB/s, while the SD card is at good 86MB/s ā 5 times faster. # SD card hdparm -tT /dev/mmcblk0 # NVMe SSD ...
Intel Neural Compute Stick 2 - AI Vision Accelerator Review - viso.ai
Intel Neural Compute Stick 2 - AI Vision Accelerator Review - viso.ai The Intel Neural Compute Stick 2 is powered by the Intel Movidius X VPU to power on-device AI applications at high performance with ultra-low power consumption.
How much power is required by Jetson Nano? : r/JetsonNano - Reddit
Reddit www.reddit.com Jetson Nano has 5W and 10W (default) power mode. So you can calculate approximate power consumption based on your requirements. Neither of these ...
How much power is required by Jetson Nano? : r/JetsonNano - Reddit
How much power is required by Jetson Nano? : r/JetsonNano - Reddit Jetson Nano has 5W and 10W (default) power mode. So you can calculate approximate power consumption based on your requirements. Neither of these ...
Graphene Battery Vs Lithium Battery : Detailed Understanding Of ...
Graphene Battery Vs Lithium Battery : Detailed Understanding Of ... Energy densityāā For example, the specific energy of graphene batteries can reach 600 Wh/kg, much higher than the 180 Wh/kg of lithium batteries. ...
All Kinds of Wearable Batteries Comparison
Graphene battery
](https://www.grepow.com/blog/all-kind-of-wearable-batteries-comparison.html#:~:text=Graphene battery)[
Solid-state battery - Wikipedia
Solid-state battery - Wikipedia Advantages Ā· Improved energy density Ā· Increase of safety and thermal stability Ā· Expanded temperature and voltage operating ranges Ā· Faster charging and improved ...
](https://en.wikipedia.org/wiki/Solid-state_battery#:~:text=Solid,Faster charging and improved)[
Solid-state batteries could power the next generation of wearable tech
Solid-state batteries could power the next generation of wearable tech Solid-state batteries also have a lower carbon footprint, making them more environmentally friendly, and they can operate across a wider ...
Solid-State Batteries: Are They the Future of Smartwatch Power
Power universalwatchcompany.com Solid-state batteries provide unrivaled advantages, such as increased safety, extended longevity, and small size. They are more appropriate for ...
medicaldesignandoutsourcing.com
Solid-state lithium microbattery tech enables next-gen wearables
wearables www.medicaldesignandoutsourcing.com Solid-state lithium microbattery technology is poised to leapfrog Li-ion and Li- poly alternatives for hearable and wearable devices.
All Kinds of Wearable Batteries Comparison
Energy harvesting can help wearable devices become more independent, at least without undue reliance on external power. Solar energy fits perfectly with smart clothing because it absorbs energy from the sun and is used directly. Thermoelectric energy harvesting can provide a solution for devices that are directly attached to the skin, such as smart warm treasures. This energy source often provides very high energy. No loss of energy can be generated while you are exercising.
All Kinds of Wearable Batteries Comparison
The energy produced by the piezoelectric method is relatively small. The kinetic energy generated by exercise can be effectively converted very little.
All-Day Smart Glasses | Magic Leap - Expand Reality
All-Day Smart Glasses | Magic Leap - Expand Reality With hot-swappable batteries, the Magic Leap 2 guarantees zero downtime, supporting continuous workflows in any environment. This feature is especially ...
](https://expandreality.io/single-product-magic-leap-2#:~:text=All,This feature is especially)[
Designing for Low Power in Battery-Operated Devices ā Omi AI
- Choose a battery that matches your device's power profile and usage scenarios. Consider lithium-ion or lithium-polymer batteries for higher energy density. * Use efficient DC-DC converters to manage voltage levels dynamically based on load requirements. * Implement voltage monitoring to switch off non-essential loads to avoid excessive drain.
Designing for Low Power in Battery-Operated Devices ā Omi AI
Optimize Peripheral Usage
Omi: An AI Wearable Aiming to Revolutionize Personal Interaction
Worn on a lanyard around the neck, attached to clothes, or even stuck to the side of the head using medical tape, Omi operates continuously with a battery life of up to three days on a single charge. Thereās no need for a wake word; think of it as primarily a companion with a touch of Alexaās assistant-like functionality. The device listens to your conversations and can:
Designing for Low Power in Battery-Operated Devices ā Omi AI
Implement Low-Power Software Techniques
Designing for Low Power in Battery-Operated Devices ā Omi AI
- Leverage interrupt-driven design to wake the processor only when necessary, rather than using polling methods.
Designing for Low Power in Battery-Operated Devices ā Omi AI
Reduce System Clock Frequency
GitHub - OwlAIProject/Owl: A personal wearable AI that runs locally
proactive assistance, or switch to offline mode in network-constrained environments. * Advanced Conversation Understanding: Utilize VAD-based conversation endpointing to segment and understand your interactions.
Designing for Low Power in Battery-Operated Devices ā Omi AI
- Choose microcontrollers specifically designed for low power consumption, such as those with ultra-low power modes. * Use components that support energy-saving modes, like e-paper displays and low-power sensors. * Select energy-efficient power management ICs to optimize power supply regulation.
Designing for Low Power in Battery-Operated Devices ā Omi AI
- Design algorithms and system tasks to minimize active duty cycle by batching tasks together. * Ensure processing tasks are completed as quickly and efficiently as possible before returning to low-power states.
GitHub - OwlAIProject/Owl: A personal wearable AI that runs locally
We are thrilled to unveil "Bee," our pioneering reference hardware device tailored for AI wearable developers and aficionados. Bee distinguishes itself with a remarkable fifty-hour battery life and a sleek, compact form factor, spanning merely 1.2 inches in diameter.
](https://github.com/OwlAIProject/Owl#:~:text=We are thrilled to unveil,2 inches in diameter)[
GitHub - OwlAIProject/Owl: A personal wearable AI that runs locally
- Fifty-Hour Battery Life: Engineered for longevity, Bee empowers you to unlock new potential by feeding your entire life as context for your personal AI, which is always by your side ready to assist. * Compact Size: At just 1.2 inches in diameter, Bee is designed to integrate seamlessly into a myriad of wearable contexts. * Community Testing Program: We are offering Bee to select contributors and those keen to delve into its potential and unlocking new use cases around productivity, memory, and agents.
](https://github.com/OwlAIProject/Owl#:~:text=* Fifty,around productivity%2C memory%2C and agents)[
Leon - Your Open-Source Personal Assistant
Leon - Your Open-Source Personal Assistant Leon is an open-source personal assistant who can live on your server. He is built on the top of Node.js, Python and artificial intelligence concepts.
](https://getleon.ai/#:~:text=Leon ,Python and artificial intelligence concepts)[
How to run a Large Language Model (LLM) on a Raspberry Pi 4 : r/raspberry_pi
Install ollama:
ChatGPT but offline and private and it runs on Raspberry Pi 5 8GB
ChatGPT but offline and private and it runs on Raspberry Pi 5 8GB This is slow compared to Cloud LLMs like ChatGPT, but if you are on-the-go and have no WiFi signal, this would be more than sufficient for answering questions.
GitHub - OwlAIProject/Owl: A personal wearable AI that runs locally
- Broad Capture Device Support: Capture your life using custom devices based on ESP platforms, Sony Spresense, or consumer hardware like the Apple Watch. Additional device support is continually added, so anything with a sensor will be able to send data to Owl. * Flexible Inference Options: Run completely locally or leverage commercial models. Perform inference using any model available on Ollama, or use commercial models like GPT-4. Utilize TTS with Whisper or choose commercial services like Deepgram. * Capture from the Web: Easily capture content from your desktop or any mobile
](https://github.com/OwlAIProject/Owl#:~:text=,your desktop or any mobile)[
GitHub - OwlAIProject/Owl: A personal wearable AI that runs locally
be able to send data to Owl. * Flexible Inference Options: Run completely locally or leverage commercial models. Perform inference using any model available on Ollama, or use commercial models like GPT-4. Utilize TTS with Whisper or choose commercial services like Deepgram. * Capture from the Web: Easily capture content from your desktop or any mobile phone. * ļø Multimodal Capture: Engage in continuous capture of location and audio. Support for images and video coming very soon!
](https://github.com/OwlAIProject/Owl#:~:text=be able to send data,and video coming very soon)[
Jan: Open source ChatGPT-alternative that runs 100% offline - Jan
Jan: Open source ChatGPT-alternative that runs 100% offline - Jan Chat with AI without privacy concerns. Jan is an open-source alternative to ChatGPT, running AI models locally on your device.
](https://jan.ai/#:~:text=Jan%3A Open source ChatGPT,models locally on your device)[
How to Run Your Own Free, Offline, and Totally Private AI Chatbot
How to Run Your Own Free, Offline, and Totally Private AI Chatbot Be your own AI content generator! Here's how to get started running free LLM alternatives using the CPU and GPU of your own PC.
How to Run Your Own Free, Offline, and Totally Private AI Chatbot
How to Run Your Own Free, Offline, and Totally Private AI Chatbot Be your own AI content generator! Here's how to get started running free LLM alternatives using the CPU and GPU of your own PC.
Humane Ai Pin Review: Too Clunky, Too Limited | WIRED
That was my mom's reaction when she saw me wearing the Humane Ai Pin. This new artificial-intelligence-enabled wearable lets you access the power of AI models such as OpenAI's 33, plus a mix of others, wherever you are with a simple tap of the finger. My mom learned about it by watching her routine Indian newsāa testament to the far-reaching buzz that Humane's first product has generated.
Top 10 Media Servers for Home Use in 2023 | ZimaSpace Blog
Blog www.zimaspace.com Jellyfin Media Server. Jellyfin is an open-source media server that allows users to organize and stream their personal media collections. It ...
](https://www.zimaspace.com/blog/top-10-media-servers-for-home.html#:~:text=Blog www,It)[
Jellyfin: The Free Software Media System
Jellyfin: The Free Software Media System Jellyfin is the volunteer-built media solution that puts you in control of your media. Stream to any device from your own server, with no strings attached.
](https://jellyfin.org/#:~:text=Jellyfin%3A The Free Software Media,server%2C with no strings attached)[
GitHub - OwlAIProject/Owl: A personal wearable AI that runs locally
Privacy and Security
](https://github.com/OwlAIProject/Owl#:~:text=Privacy and Security)[
GitHub - OwlAIProject/Owl: A personal wearable AI that runs locally
intercepted, allowing access to all conversations stored on the server. Recommended practices to improve security:
RaspberryPi Powered Wearable Computer : 8 Steps (with Pictures) - Instructables
Battery Recharger (came with 4xAA batteries) - $35 at Radioshack 6 xAA Recahrgeable Batteries - $7 at Radioshack RaspberryPi Model B - $45 including shipping Soft Cellular Phone Case - $5 at 5 and below. USB Battery Pack - MintyBoost Kit - $20 - https://www.adafruit.com/products/14 2xAA battery holders - $5 Vuzix Wrap 920 Video Glasses - $200 on Amazon - http://www.amazon.com/Vuzix-329T30011-Corp-Wrap-920/dp/B002SUCMUG/
RaspberryPi Powered Wearable Computer : 8 Steps (with Pictures) - Instructables
6 xAA Recahrgeable Batteries - $7 at Radioshack RaspberryPi Model B - $45 including shipping Soft Cellular Phone Case - $5 at 5 and below. USB Battery Pack - MintyBoost Kit - $20 - https://www.adafruit.com/products/14 2xAA battery holders - $5 Vuzix Wrap 920 Video Glasses - $200 on Amazon - http://www.amazon.com/Vuzix-329T30011-Corp-Wrap-920/dp/B002SUCMUG/ Mouse/Keyboard - $37 on Amazon - http://www.amazon.com/VisionTek-Candyboard- Mini-Keyboard-Touchpad/dp/B007VMCBN4/
[PDF] jetson-orin-nano-datasheet-r4-web.pdf - Open Zeka
[PDF] jetson-orin-nano-datasheet-r4-web.pdf - Open Zeka NVIDIAĀ® Jetson Orin Nanoā¢-series modules deliver up to 40 TOPS of Al performance ... Jetson runs the NVIDIA AI software stack, and use-case specific application.
Jetson Nano vs Raspberry Pi vs MiniPC : r/ROS - Reddit
In my opinion, choosing the Raspberry Pi 4x seems like a cost-effective option compared to the Jetson Nano. If your project involves object ...
Open Source Battery Pack
Open Source Battery Pack A 5-cell (18650) 18v battery holder similar to a "AA" battery holder, designed to connect safely and easily with bus-bars into larger high-ampage packs.
meet omi, a wearable AI device that can read minds
Thereās a new wearable AI device in town, and it goes by the name Omi. It looks like a big button that people can stick onto their foreheads. Once set, it starts to read minds and completes the tasks 33 think of before they can even say them out loud. It can also hear conversations, and once it does, it informs the users what theyāre about and helps them answer back, in case needed. The wearable AI device Omi connects to the usersā smartphones before it starts rolling. Then, they think of what they want to think, and the device replies through the dedicated app.
meet omi, a wearable AI device that can read minds
Itās somewhat a personal assistant, one that lives for the mind. It either sits on the side of the forehead or around the neck as a necklace. It can hear voices from the surrounding people, but users can make it only listen to them. These conversations are stored locally on the smartphone or on the cloud, but users are more than welcome to delete everything in one click. So far, the wearable AI device Omi attempts to read the minds using a brain interface module inside it. The team adds that it may be able to read data from the brain with a separate brain-interface module.
Omi: An AI Wearable Aiming to Revolutionize Personal Interaction
life of up to three days on a single charge. Thereās no need for a wake word; think of it as primarily a companion with a touch of Alexaās assistant-like functionality. The device listens to your conversations and can:
Omi: An AI Wearable Aiming to Revolutionize Personal Interaction
Worn on a lanyard around the neck, attached to clothes, or even stuck to the side of the head using medical tape, Omi operates continuously with a battery life of up to three days on a single charge. Thereās no need for a wake word; think of it as primarily a companion with a touch of Alexaās assistant-like functionality. The device listens to your conversations and can:
Omi: An AI Wearable Aiming to Revolutionize Personal Interaction
- Integrate with Apps and Services: With an open-source app store featuring over 250 apps, developers can create integrations that expand Omi's capabilities, such as connecting to Zapier or Google Drive.
Humane Ai Pin Review: Too Clunky, Too Limited | WIRED
camera, then told me I was staring at lettuce. (Correct!) My mom asked me to peel a cucumber, so I tapped the device to wake it up, then asked, āDo you need to peel a cucumber?ā The Pin said it's unnecessary unless the skin has been waxed or you plan to cook with it. My mom said she was making fish molee, so I asked the Pin what it was and, to our surprise, it understood and answered:
Humane Ai Pin Review: Too Clunky, Too Limited | WIRED
TIRED
](https://www.wired.com/review/humane-ai-pin/#:~:text=TIRED)[
Humane Ai Pin Review: Too Clunky, Too Limited | WIRED
Scarce features at launch. Thermal issues cause overheating. Accuracy of answers is mixed (and it's slow). Projector is annoying to interact with and is impossible to see in daylight. Poor photos and videos in low light. Can't sync the Ai Pin's number to a cell number. Easy for others to hijack or steal.
Humane Ai Pin Review: Too Clunky, Too Limited | WIRED
$699 at Humane
](https://www.wired.com/review/humane-ai-pin/#:~:text=%24699 at Humane)[
Buy Ai Pin | Humane
Buy Ai Pin | Humane The price for Ai Pin starts at $499. Taxes and fees not included. Ai Pin requires an active Humane Plan. $24/month, taxes and fees not included.
](https://humane.com/shop#:~:text=Buy Ai Pin ,taxes and fees not included)[
Humane Ai Pin Review: Too Clunky, Too Limited | WIRED
Information
](https://www.wired.com/review/humane-ai-pin/#:~:text=Information)[
GitHub - OwlAIProject/Owl: A personal wearable AI that runs locally
on ESP platforms, Sony Spresense, or consumer hardware like the Apple Watch. Additional device support is continually added, so anything with a sensor will be able to send data to Owl. * Flexible Inference Options: Run completely locally or leverage commercial models. Perform inference using any model available on Ollama, or use commercial models like GPT-4. Utilize TTS with Whisper or choose commercial services like Deepgram. * Capture from the Web: Easily capture content from your desktop or any mobile phone.
GitHub - OwlAIProject/Owl: A personal wearable AI that runs locally
- Wearable capture devices. These include semi-custom development boards (with some assembly required) as well as off-the-shelf products like Apple Watch. We would like to develop fully custom open source hardware. 2. AI server. 3. Presentation clients. Applications that display information gathered by the system (e.g., transcripts, conversation summaries) and allow interaction with an online assistant. Currently, a mobile app and web app are included.
](https://github.com/OwlAIProject/Owl#:~:text=1,and web app are included)[
GitHub - OwlAIProject/Owl: A personal wearable AI that runs locally
- Broad Capture Device Support: Capture your life using custom devices based on ESP platforms, Sony Spresense, or consumer hardware like the Apple Watch. Additional device support is continually added, so anything with a sensor will be able to send data to Owl.
](https://github.com/OwlAIProject/Owl#:~:text=,to send data to Owl)[
Wearable Computing
Image: Rockwell Trekker 9 In the late 1990s, Rockwell produced the TREKKER, a rugged wearable computer, based on a 120 MHz Pentium, with built-in DSP support for speech interface and a monocular head-worn display. It was a first start of commercially available wearable computing hardware, and it had its niche application in the manufacturing shop floor. I was lucky to get one of these expensive units ($10,000) as part of my work at the Rockwell Science Center and to explore its capabilities in our research activities.
Wearable Computing
Image: Xybernaut MA IVAs the CPUs and motherboards became smaller and more integrated, more powerful solutions became available. The Xybernaut Mobile Assistant followed the design of the Rockwell Trekker - although significantly smaller - and provided more computing power. Image: modified camera mount This wearable computer came with a camera that could be mounted near the head-worn monocular display. We modified the mounting to provide a more sturdy fixation, with the possibility to change the pitch angle. The computing power of this PC encouraged us to build the wearbale testbed WIMMIS (see below).
Where It ā BODY MAPS ā Clint Zeagler
- Weight, Load, or the pressure of weight should be placed on the fleshy, but non-sensitive parts of the body, avoiding boney areas. * The lower waist is a good area for heavy loads. * Weight should be balanced across the body evenly, and aligned to the center of gravity is possible. * Heavy items should not be placed on the bodyās extremities for long periods of time. * Batteries for wearable device tend to be the source of most of the weight. If a device needs a large battery (to last a long time or because it needs large
](https://www.clintzeagler.com/where-it-body-maps/#:~:text=,or because it needs large)[
magicleap.care
Battery Life ā Care - Magic Leap
The compute pack's battery can last about 3 and a half hours of continuous use. If you are using a heavier compute application, the battery will ...
meet omi, a wearable AI device that can read minds
informs the users what theyāre about and helps them answer back, in case needed. The wearable AI device Omi connects to the usersā smartphones before it starts rolling. Then, they think of what they want to think, and the device replies through the dedicated app.
Wearable Tech/Pip-Boy Inspired build Discussion : r/cyberDeck
Wearable Tech/Pip-Boy Inspired build Discussion : r/cyberDeck A custom unit that is built to fit the arm that uses a slide-off display (when closed covers the keyboard) as a forearm mounted PC would be a ...
All Kinds of Wearable Batteries Comparison
Graphene batteries are considered to have the highest energy density of all current battery types. Graphene batteries also have high power storage capabilities.
RaspberryPi Powered Wearable Computer : 8 Steps (with Pictures) - Instructables
the microUSB cable to the Pi.Ā The extra bits of the microUSB cable can be shoved into your pocket.Ā I put the video glasses control box in a separate
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static.designboom
Introduction
Building a wearable Personal Area Network (PAN) device with a local AI assistant and personal media server requires carefully balancing form factor, performance, battery life, and cost. This report examines form factor options, hardware components, power solutions, open-source software, budget considerations, and upgradeability. It also draws inspiration from industry examples like the Omi AI medallion and other wearable AI devices. The goal is to identify an optimal prototype design that is efficient, cost-effective, comfortable for long use, and easy to use and upgrade.
1. Form Factor Analysis
Wearable Form Factors Considered: We explore four main form factors ā a belt/waist pack, a vest, a bracer (forearm unit), and a helmet-style or head-mounted unit ā evaluating each for portability, comfort, efficiency, and upgradeability.
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Belt or Waist Pack: Mounting the device at the waist (via a belt clip, fanny pack, or strap) offers good weight distribution and comfort for heavier components. The human waist/hip area can support more weight without discomfort, which is why wearable designers note āthe lower waist is a good area for heavy loadsā (Where It ā BODY MAPS ā Clint Zeagler) (Where It ā BODY MAPS ā Clint Zeagler). Batteries or computing units worn on a belt will feel lighter than the same weight on an arm or head. A belt form factor can be discreet (hidden under a jacket) and keeps hands and head free. It also allows relatively easy access to the device for maintenance or upgrades. Many early and modern wearable PCs use belt-mounted units for this reason (e.g. the 1990s Xybernaut belt-worn computer (Wearable Computing) and hobby projects where a Raspberry Pi is clipped to the belt (RaspberryPi Powered Wearable Computer : 8 Steps (with Pictures) - Instructables) (RaspberryPi Powered Wearable Computer : 8 Steps (with Pictures) - Instructables)). Trade-offs: A belt pack might require a cable to peripherals (e.g. to head-worn displays or pocket interfaces), and if not secured it could bounce during movement. Overall, a belt/waist unit is highly efficient for weight and heat dissipation, and likely the best choice for a first prototype given its balance of comfort and capacity.
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Vest or Chest Rig: A vest can distribute components across the torso, with pockets or pouches for the computer, battery, etc. This spreads weight evenly and keeps the center of gravity close to the body (Where It ā BODY MAPS ā Clint Zeagler) (Where It ā BODY MAPS ā Clint Zeagler). A wearable vest might integrate wiring channels and mounting points for modules, making it somewhat modular and upgradable. For example, military and industrial wearable systems often use a vest or harness to carry radios, battery packs, and computing units. Trade-offs: Wearing a vest is less casual ā it adds bulk and might be warm to wear for long periods. Itās also less discreet than a belt pack. However, a vest could allow a larger battery or additional sensors that a smaller form factor couldnāt comfortably carry. For a high-performance prototype (with larger batteries or multiple devices), a lightweight vest or shoulder holster might be appropriate to maintain comfort.
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Bracer (Forearm Device): A bracer is a device strapped to the forearm, akin to a robust smartwatch or a āPip-Boyā style computer. This form offers direct access to a screen or controls on your arm, which can be useful for quick interactions. Modern examples include rugged PDA devices strapped to an arm for warehouse picking or military use (Dragonfly Wearable Computer - AML). It maximizes portability (your arm carries it) and keeps the device in view. Trade-offs: The forearm is considered an extremity, and ergonomics research advises against placing heavy devices on extremities for long periods (Where It ā BODY MAPS ā Clint Zeagler). A bracer is best for lightweight builds ā if the core system can be miniaturized (small SBC and a small battery). A heavy forearm device would cause fatigue. Additionally, upgradeability can be limited by space ā thereās only so much you can strap to your arm. For our use-case (AI and media server), a bracer likely cannot accommodate the required battery and storage unless the performance requirements are scaled down. It might be a secondary option for a very minimal āliteā version of the device.
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Helmet or Head-Mounted: Integrating the system into a helmet or head-mounted unit (like an AR headset or hard-hat with electronics) is the least portable and most intrusive option for everyday personal use. While head-mounted systems (e.g. HoloLens or Google Glass) put the interface and sensors on the head, they typically offload processing to a belt pack or phone to avoid weight and heat on the head. A helmet form factor could make sense for augmented reality with integrated displays, but for a personal AI server, a helmet would add unnecessary strain. Users generally canāt comfortably carry more than a few hundred grams on the head for long. Trade-offs: A helmet could house small components and benefit from line-of-sight for sensors (cameras at eye level, etc.), but the discomfort and lack of modularity are major downsides. Itās also not socially convenient in normal settings. We consider this form factor impractical for our goals, except perhaps as a niche prototype for AR specialists.
Recommended Form Factor: For a balanced prototype emphasizing efficiency, ease of use, and upgradability, a belt-mounted or waist pack device is the top choice. It leverages the waistās ability to carry weight comfortably (Where It ā BODY MAPS ā Clint Zeagler), allows a reasonably large battery and computing module, and can connect easily to peripherals (like smart glasses or a handheld) via short cables or wireless links. A vest could be a close second if more equipment needs to be carried, but itās more complex to wear. The bracer and helmet forms are less ideal given current hardware needs. In summary, a waist/hip-worn module (perhaps the size of a small external battery pack or paperback book) offers the best mix of portability and capability for a personal AI hub.
2. Hardware Components for Computing and Connectivity
Designing the wearable PAN device involves selecting core computing hardware and support components that can handle AI processing and media server duties within tight power and size constraints. Below we evaluate various single-board computers (SBCs) and related hardware, storage solutions, and connectivity options:
-
Computing Hardware (SBCs and Modules): Modern SBCs provide desktop-class capabilities in small sizes, making them suitable for wearable AI devices. A popular baseline is the Raspberry Pi. A Raspberry Pi 4 or the newer Pi 5 offers a good balance of cost, size, and community support. The Pi 4 Model B has a 1.5Ā GHz 4-core ARM Cortex-A72 CPU and VideoCore VI GPU, and the Pi 5 upgrades to a 2.4Ā GHz 4-core Cortex-A76 (significantly faster) (Raspberry Pi 5 with NVMe - Wolf Paulus). Raspberry Pis are affordable ($35ā75 range) and can run Linux easily, but they have limited AI acceleration (no built-in neural accelerator). NVIDIAās Jetson series is a stronger candidate for AI tasks: for example, the Jetson Nano (4 Cortex-A57 cores + a 128-core Maxwell GPU) delivers around 472 GFLOPs of AI compute and can run many common AI frameworks (Jetson NANO 4GB B01(Official/SUB) Developer Kit - Yahboom). In practice, Jetsonās GPU makes it 3ā5Ć faster than Raspberry Pi 4 for neural network tasks like image recognition or training small models (Training ANNs on the Raspberry Pi 4 and Jetson Nano). Newer Jetson models (Jetson Xavier NX or Jetson Orin Nano) provide even more AI performance (e.g. ~21 TOPS on the Xavier NX at 10Ā W) (NVIDIAĀ® Jetson Xavier⢠NX Module 16 GB - Seeed Studio), though at higher cost and power draw. Other SBC options include the BeagleBone AI-64 (with Texas Instruments SoC and embedded AI cores), various Rockchip RK3588-based boards (8-core CPU with built-in NPU ~3 TOPS), or even ultra-compact x86 boards like Intel NUC compute elements or LattePanda which can run PC-grade CPUs. Comparison: Raspberry Pi excels in cost-effectiveness and community support, Jetson and RK3588 boards excel in on-device AI processing, and x86-based boards excel in general compute power and compatibility. For a personal AI assistant that may run local language models or computer vision, having an accelerator or strong GPU is beneficial. A Jetson Nano or Orin Nano would outperform a Pi for AI, but will use more power. An Intel/AMD NUC or mini-PC (e.g. a 11th-gen Core i5 NUC board) could rival Jetsonās AI performance via CPU and possibly iGPU, but typically draw 15ā25Ā W or more and cost several hundred dollars. Recommendation: A reasonable mid-range choice is an ARM-based SBC with an AI accelerator ā for example, Raspberry Pi 5 coupled with a Google Coral Edge TPU (4 TOPS) via USB, or a Jetson Nano/Orin which has built-in GPU for AI. This would handle moderate AI tasks locally. High-end builds might use a Jetson Xavier/Orin or an x86 SBC with a powerful CPU for maximum capability, while low-cost builds might stick to Raspberry Pi or similar.
-
Memory and Storage Options: For smooth AI and server operations, adequate RAM and fast storage are important. At least 4Ā GB RAM is recommended (8Ā GB or more for running larger language models or caching media). Most SBCs (Pi, Jetson, etc.) come with 4ā8Ā GB RAM variants. In terms of storage, a combination of fast solid-state storage and expandable modules works best. SBCs like Raspberry Pi boot off microSD by default, but microSD cards have limited speed and durability (the Pi 4 can achieve ~50ā90Ā MB/s on a good SD card). Newer boards allow faster storage: the Raspberry Pi 4 can boot from USB 3.0 drives, and the Pi 5 introduces an M.2 PCIe interface via an add-on board for NVMe SSDs. An NVMe SSD on Pi 5 can reach ~700ā800Ā MB/s read speeds, which is nearly an order of magnitude faster than microSD performance (Pimoroni NVMe Base for Raspberry Pi 5 Review: NVMe for Me | Tom's Hardware). For a personal media server holding music, videos, or documents, using a solid-state drive (SSD) is ideal for reliability and speed. Options include a small SATA or NVMe SSD (256Ā GB, 512Ā GB, or 1Ā TB as needed) either in an internal M.2 slot or connected via USB 3.0 enclosure. Many compact boards (and all NUCs) support internal SSDs (either M.2 or 2.5ā SATA) ā these provide modular, upgradeable storage that can be swapped as the media library grows. External vs Internal: External USB drives are easy to attach and remove, but add a cable and bulk; internal (onboard) storage like an eMMC module or NVMe drive is more integrated. For a wearable, internal or securely attached storage is preferable to avoid cords. Some SBCs (Jetson NX, Orin, certain Pi compute module carriers) even support NVMe directly on the board, which is optimal. Additionally, modular NVMe carriers (for example, Raspberry Piās official NVMe adapter or third-party HATs) can be used to add SSD storage to boards that support it (4-way NVMe RAID comes to Raspberry Pi 5 | Jeff Geerling) (Raspberry Pi 5 with 52Pi NVMe hat, Samsung 990 EVO SSD). These allow future upgrades by simply swapping the NVMe drive. In summary, plan for a fast primary storage (SSD) for OS, AI models, and media, supplemented by a microSD or secondary storage if needed for less speed-critical data. For low-cost builds, a high-endurance microSD (128+Ā GB) can work initially, but the user may quickly hit limits with media files.
-
Connectivity Components (PAN and Beyond): A wearable hub should communicate with various personal devices (smartphone, AR glasses, wireless earbuds, etc.). Key wireless radios to include are Wi-Fi and Bluetooth. Wi-Fi (preferably dual-band 2.4/5Ā GHz or even Wi-Fi 6) enables high-speed data transfer for media streaming and internet connectivity. Bluetooth (BLE 5.0 or newer) allows interfacing with wearables like smart glasses, headphones, or input devices. Most SBCs like Raspberry Pi have Wi-Fi 5 and BLE 5 built-in. Others (like Jetson Nano dev kit) may require adding a Wi-Fi/BT module (often via M.2 or USB). Personal area network connectivity can be expanded with technologies like NFC and UWB. NFC could enable quick pairing or data transfer with phones (for example, tap the device to a phone to share a file or authenticate). Ultra-Wideband (UWB) is an emerging tech for precise ranging ā itās used in AirTags and some phones for spatial awareness. While not common on maker boards yet, UWB modules could be added via SPI for experimental use, allowing the wearable to act as a locator or to do gesture detection. Wired connectivity is also important for versatility: USB ports for peripherals (e.g. connecting a camera or keyboard), and possibly video output if the device needs to drive glasses or a heads-up display. SBCs typically provide USB (the Pi has 2Ć USB 3.0, 2Ć USB 2.0; Jetsons have USB and sometimes PCIe lanes). If the user plans to connect AR glasses that use USB-C video (like some smart glasses do), ensure the board can output display signals or support the glassesā protocol. 5G / Cellular: To make the device truly ubiquitous, cellular connectivity is an option. This could be achieved by tethering to a smartphone (simplest way) or by integrating a cellular modem module. There are LTE/5G modem HATs and USB dongles compatible with Raspberry Pi and Linux. For example, a 4G LTE hat or a USB 5G modem can give the wearable independent internet access, albeit with added cost and power draw. This is optional for most use-cases (most users carry a phone that can hotspot), but worth noting for a high-end autonomous setup. In summary, the core connectivity will be Wi-Fi and Bluetooth for local networking and accessory pairing, with expandability to NFC, UWB, or cellular if needed. All these components should be chosen in low-power versions (e.g. BLE for sensors instead of continuous Wi-Fi where possible) to conserve energy.
-
CPUs, GPUs, and AI Accelerators (Balancing Power vs Performance): Since an AI assistant is a core function, hardware acceleration for AI workloads is a major consideration. CPUs: ARM CPUs (like on Pi or Jetson) are efficient per watt, and modern ones can handle moderate AI inference, especially with quantized models. GPUs: A GPU (like the 128-core in Jetson Nano or the more powerful Ampere GPU in Jetson Orin) greatly speeds up neural network tasks by parallel processing. AI Accelerators: Specialized NPUs or VPUs can deliver high AI performance at low power. Examples include Googleās Edge TPU (USB or M.2 form, 4Ā TOPS at ~2Ā W), Intelās Neural Compute Stick 2 (Movidius Myriad X VPU, ~1Ā TOPS at <1Ā W), or Hailo-8 (a mini PCIe module boasting 26Ā TOPS at 2.5Ā W). These accelerators can be paired with an SBC ā for instance, a Raspberry Pi with a Coral USB accelerator can outperform the Pi alone by an order of magnitude on vision tasks. There are also boards like the Rockchip RK3588-based ones which include a built-in NPU (e.g. ~3Ā TOPS on the RK3588) for basic AI tasks without extra hardware. Energy Efficiency: To maximize battery life, one should seek components that give the needed compute within a tight power envelope. Using an accelerator to offload AI from the CPU can be more energy-efficient (doing in 1Ā second what the CPU might do in 10Ā seconds at full load, then returning to idle). For example, Intelās Movidius VPU is āultra-low powerā for on-device vision and runs at a few hundred mW when doing inference (Intel Neural Compute Stick 2 - AI Vision Accelerator Review - viso.ai). Similarly, the Jetson Nano has a 5Ā W low-power mode for lighter loads, and can scale to 10Ā W for heavier tasks (How much power is required by Jetson Nano? : r/JetsonNano - Reddit). Thermal considerations: Wearable devices canāt have large fans. Many SBCs can be underclocked or put into efficient modes ā Jetsons have configurable power modes (5Ā W, 10Ā W etc.) (How much power is required by Jetson Nano? : r/JetsonNano - Reddit). Itās wise to choose a board that can run fanless or with a small heatspreader without overheating during typical use. In summary, the hardware selection should prioritize an efficient AI-capable processor. A baseline might be Raspberry Pi 4/5 (for cost) accepting slower AI performance; a mid-tier might be Jetson Nano or a Pi + EdgeTPU combo (for moderate AI ability); and a high-end might be Jetson Xavier/Orin or an x86 with a dedicated accelerator (for heavy models). This ensures the personal assistant can run local tasks (like speech recognition, small LLM inference) and the media server can transcode or stream data as needed.
3. Power Solutions (Battery and Power Management)
Power is the lifeblood of a wearable device ā it needs to run untethered for extended periods. Key considerations include battery type, capacity, charging, and power management strategies to maximize uptime.
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Battery Choices: The go-to batteries for wearable electronics are Lithium-based rechargeable cells due to their high energy density. Lithium-ion (Li-ion) and Lithium-polymer (Li-Po) batteries are common; they offer good energy-to-weight ratio and come in various shapes (prismatic cells, cylindrical 18650 cells, flat pouch cells). Li-ion 18650 cells (as used in laptop batteries) are attractive for DIY ā they are energy-dense and cheap, and multiple cells can be combined for higher capacity or voltage. Li-Po pouch cells can be flat and fit against the body more comfortably (often used in smartphones and VR headset battery packs). For example, a single 18650 Li-ion cell (3.7Ā V nominal) might provide ~3000Ā mAh; using two in parallel doubles capacity (~3.7Ā V, 6000Ā mAh), or using two in series yields 7.4Ā V for devices needing higher voltage. Most SBCs run at 5Ā V (Raspberry Pi, Jetson Nano) or 7ā12Ā V (some x86 boards), so usually a regulated power supply or boost converter is needed. Many wearable builds use a pack of Li-ion cells with a DC-DC converter to output a stable 5Ā V via USB. Advanced Battery Tech: Emerging technologies like graphene-enhanced batteries and solid-state batteries promise even better performance. Graphene batteries are reported to have dramatically higher energy density (potentially 2ā3Ć Lithium-ion) (Graphene Battery Vs Lithium Battery : Detailed Understanding Of ...), meaning more run-time for the same weight, and they can charge extremely fast. However, graphene batteries are not yet mainstream ā they are mostly in prototype or high-cost phases (All Kinds of Wearable Batteries Comparison). Solid-state batteries replace the liquid electrolyte with solid material; they are safer (non-flammable) and can pack more energy in the same volume with longer lifespan (Solid-state battery - Wikipedia) (Solid-state batteries could power the next generation of wearable tech). Companies are working on solid-state microbatteries for wearables that could significantly boost smartwatch and headset endurance (Solid-State Batteries: Are They the Future of Smartwatch Power) (Solid-state lithium microbattery tech enables next-gen wearables). For now, a practical prototype will rely on Li-ion/Li-Po cells, possibly with multiple cells to get the desired capacity. Capacity and Life: To estimate, consider target usage time ā e.g. 8 hours continuous use. If the device averages 5Ā W consumption, thatās 40Ā Wh needed for 8h (which could be a single 3.7Ā V, ~11000Ā mAh cell ā impractically large ā or more likely a pair of 18650s at 7.4Ā V, ~6000Ā mAh, which is ~44Ā Wh). Many DIY wearables use ~Ā 10,000 mAh at 5Ā V (which is essentially a common power bankās capacity) to get through a workday with some margin.
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Power Supply and Charging Systems: For safety and convenience, itās wise to leverage existing charging standards. USB-C Power Delivery (PD) is ideal ā it can handle the needed power and is ubiquitous. A custom device can incorporate a USB-C PD module that negotiates a 9Ā V or 12Ā V input if needed, or simply use 5Ā V if all electronics are 5Ā V logic (most SBCs are 5Ā V). Many hobbyists repurpose phone power banks for wearables, since they include charging circuits and regulated output. For a cleaner integration, one could use a Lithium battery management board that handles charging (from a USB source) and provides a regulated output. For example, Adafruitās PowerBoost or similar modules can boost a 3.7Ā V Li-Po to 5Ā V and allow charging simultaneously. Ensure the system supports charging while operating (passthrough) so that you can recharge the device without shutting it down ā this way a user can top-up from a wall outlet or a bigger pack in their bag when needed. Alternative Charging: Since itās a wearable, adding solar assist is a possibility ā small flexible solar panels on a vest or backpack could trickle charge the battery during the day. Realistically, solar panels of wearable size wonāt fully sustain the system, but they might add a few hundred mW in sunlight, extending runtime a bit. Another emerging idea is energy harvesting (thermal, kinetic), but those produce very little power (more suitable for ultra-low-power sensors) (All Kinds of Wearable Batteries Comparison) (All Kinds of Wearable Batteries Comparison). Thus, the main charging method remains plugging in. A hot-swappable battery design is worth considering for enterprise-style use (like how Magic Leap 2 allows an external pack swap to keep running (All-Day Smart Glasses | Magic Leap - Expand Reality)), but for a personal prototype, swapping packs may be too complex.
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Power Management Best Practices: Efficient power management can dramatically prolong operation. Both hardware and software strategies are used. On the hardware side, voltage regulation should be done with efficient DC-DC converters (switching regulators) instead of linear regulators, to minimize wasted heat ( Designing for Low Power in Battery-Operated Devices ā Omi AI). Power-hungry components like displays or sensors should be turned off or put in low-power mode when not in use ( Designing for Low Power in Battery-Operated Devices ā Omi AI). If using a microcontroller or secondary processor, it can cut power to subsystems via load switches when those are idle. On the software side, utilize low-power states aggressively. For instance, the system could stay mostly idle and wake on voice command or on a schedule. The Omi AI wearable, which boasts up to 3 days of battery life, likely achieves this through aggressive low-power design ā it continuously listens for trigger phrases and logs, but likely offloads heavy computation to the phone/cloud, keeping the wearableās own processing minimal (Omi: An AI Wearable Aiming to Revolutionize Personal Interaction). In our local AI case, we might not offload to cloud, but we can still optimize: run the AI models at the lowest acceptable frequency, and avoid polling when event-driven triggers suffice ( Designing for Low Power in Battery-Operated Devices ā Omi AI) ( Designing for Low Power in Battery-Operated Devices ā Omi AI). Another tip is dynamic frequency scaling (DVFS) ā adjusting CPU/GPU speed based on load ( Designing for Low Power in Battery-Operated Devices ā Omi AI). Linux on SBCs supports CPU governor modes; one can cap the CPU speed to reduce consumption when full power isnāt needed. For example, if only light background tasks are running, scaling down to half speed can save energy and then turbo boost when voice queries come in. Thermal management is also indirectly power management: preventing thermal throttling by distributing tasks or having adequate cooling means the system can finish tasks faster and return to idle. Lastly, keeping the duty cycle of intensive tasks low is key ā e.g., processing microphone input in short bursts (using voice activity detection to only process when someone is speaking) (GitHub - OwlAIProject/Owl: A personal wearable AI that runs locally), or scheduling media server transcoding at times when the device can be connected to power. In summary, a combination of selecting low-power components (e.g. an MCU for sensor monitoring, efficient sensors, low-power modes) ( Designing for Low Power in Battery-Operated Devices ā Omi AI) and smart software (sleep modes, batched processing) ( Designing for Low Power in Battery-Operated Devices ā Omi AI)will maximize battery life. Practical measures include: disabling Wi-Fi/Bluetooth when not needed, dimming or not using LEDs, and providing user feedback on battery status so they can recharge proactively. With these practices, a well-optimized wearable could run many hours. For instance, the open-source āBeeā reference device (from the Owl project) manages 50-hour battery life in a tiny form factor by using an ultra-low-power design and presumably very efficient data capture methods (GitHub - OwlAIProject/Owl: A personal wearable AI that runs locally) (GitHub - OwlAIProject/Owl: A personal wearable AI that runs locally).
4. Open-Source Software Solutions (OS, AI, Media, Security)
The device will need a robust and lightweight software stack, from the operating system to applications for AI assistance and media serving. Open-source software is ideal to keep costs down and ensure customizability. We break this down into the OS, AI assistant software, media server software, and security considerations:
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Operating System Options: A Linux-based OS is the primary choice for such a device, given broad hardware support and open-source flexibility. For SBCs like Raspberry Pi or Jetson, common OS choices are Raspberry Pi OS (Debian-based), Ubuntu, or specialized minimal Linux distributions. Embedded-optimized Distros: One could use a slim Linux such as DietPi (a lightweight Debian fork for Pi and other boards) which is optimized for low-resource environments, or Armbian if using ARM boards (Armbian provides streamlined Ubuntu/Debian images for many SBCs). These give you a full Linux environment but without bloat, which helps with both performance and storage usage. Another approach is using Yocto Project or Buildroot to create a custom minimal Linux tailored to only include needed features (this has a higher learning curve but results in a very efficient system). For an x86-based build, a minimal Ubuntu Server or even a specialized distro like Alpine Linux (known for its small footprint) could work. Real-time operating systems (RTOS) like FreeRTOS are generally too minimal for running an AI assistant or media server, so Linux is more practical. Some projects use Android (AOSP) as an OS for wearables, but in our case a server/assistant is better served by a standard Linux environment. User Interface: Since this device may often be headless (no direct screen, using voice or network control), we can run it as a headless server OS with no graphical desktop to save resources. If a GUI is needed for setup or debugging, lightweight environments (like LXDE or XFCE) are available. In summary, an open-source Linux OS is recommended, with choices like a minimal Ubuntu or Raspberry Pi OS Lite ā these support all the necessary open-source packages for AI and media and have strong community support.
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AI Assistant Software: For the local AI assistant functionality, we need software to handle voice interaction (speech recognition and synthesis) and an on-device language model or dialogue system. There are several open-source voice assistant frameworks to consider:
- Mycroft AI ā A popular open-source voice assistant framework that runs on Linux (it was used in Mycroft Mark II devices built on Pi). Mycroft provides voice wake word detection, STT (speech-to-text), intent parsing, and TTS (text-to-speech), with a skill system for extending capabilities. It can work offline with some configuration (using local STT like PocketSphinx or Vosk, and local TTS like Mimic). However, Mycroftās core team has had ups and downs, and the community has forked it into projects like OpenVoiceOS after Mycroft.aiās company issues. Still, itās a rich starting point.
- Leon ā Leon is an open-source personal assistant that is based on Node.js and Python, allowing you to self-host a voice/text assistant (Leon - Your Open-Source Personal Assistant). Itās modular and can run entirely offline, performing tasks like controlling applications, answering questions (with integrated AI models), etc. Leon might require some heavy lifting to integrate a local LLM, but itās designed to run on a personal server.
- Home Assistant + Add-ons ā While Home Assistant is more for home automation, it has components for voice assistants (like Almond/Genie or Rhasspy) that could be repurposed for a personal wearable context. This might be overkill if not using IoT devices, but worth mentioning.
- Custom LLM-based Assistant: Another approach is to build a custom pipeline: use OpenAIās Whisper or Mozilla DeepSpeech for speech-to-text, a small local LLM (large language model) for understanding and response, and a TTS engine like eSpeak or Coqui TTS for voice output. For example, one could run a quantized 4-billion-parameter model (or smaller) using libraries like
llama.cpp
or the Ollama server (How to run a Large Language Model (LLM) on a Raspberry Pi 4 : r/raspberry_pi) which simplifies running Llama models on local hardware. Recent developments allow running models like Llama 2 7B or distilled versions on devices like Raspberry Pi 5 (albeit slowly) (ChatGPT but offline and private and it runs on Raspberry Pi 5 8GB). There are ultra-small models such as GPT-2 variants or LLaMA 2 7B quantized to 4-bit that can fit in 4ā8Ā GB RAM. One community model, TinyLlama (1.1B) or TinyVicuna, could potentially run on a Pi-class device with <4Ā GB RAM usage. The Owl project (OwlAIProject) is an open experiment for wearable AI that uses local models via Ollama and can capture conversations (GitHub - OwlAIProject/Owl: A personal wearable AI that runs locally) (GitHub - OwlAIProject/Owl: A personal wearable AI that runs locally) ā this could be directly relevant. Owlās approach is to use whatever model you load (could be a smaller LLM for local use) and they emphasize modular capture and processing. Using such frameworks, our device could transcribe audio, feed it to a local LLM for an answer, and speak the answer back, all without cloud connectivity. - Examples of Personal LLM Assistants: Projects like āJanitor AIā or āGPT4Allā have offline modes; also the āJanā project (jan.ai) is aiming to be an offline ChatGPT alternative (Jan: Open source ChatGPT-alternative that runs 100% offline - Jan). There is also a recent interest in running chatbots offline (How to Run Your Own Free, Offline, and Totally Private AI Chatbot); PCMag and others have tutorials on running local ChatGPT-like models (How to Run Your Own Free, Offline, and Totally Private AI Chatbot) which we can adapt. For a wearable, weāll likely use a lightweight conversation model ā possibly something like an instruct-tuned 3B or 7B model with 4-bit quantization to fit in memory. The expectation should be managed: local models wonāt be as powerful as cloud GPT-4, but they can handle many personal assistant queries (taking notes, simple Q&A, summarizing text, etc.) with reasonable speed if optimized.
In addition to the ābrainā of the AI, we need wake word detection (to know when the user is speaking to it) and audio pipeline management. There are open-source wake-word engines like Porcupine (freemium) or Snowboy (discontinued but works offline), or one can train a small model with TensorFlow Lite for a custom wake word. Alternatively, run the microphone always and use a lightweight voice activity detector to trigger the STT (as done in Omi and Owlās approach, which use always-on capture and then segment speech (GitHub - OwlAIProject/Owl: A personal wearable AI that runs locally)).
Summary of AI Software: A viable stack is: Linux OS + Mycroft/Leon or custom pipeline + an LLM (like Llama 2 7B or smaller) + local STT/TTS. All components have open-source options. This ensures the assistant can function offline, aligning with the privacy goals of a personal server. (Notably, the Humane AI Pin device uses cloud AI models (Humane Ai Pin Review: Too Clunky, Too Limited | WIRED), but our goal is to avoid reliance on cloud.)
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Personal Media Server Software: For the media server functionality (hosting personal music, videos, files and streaming them to the userās other devices), there are excellent open-source solutions:
- Jellyfin: Jellyfin is a fully open-source media server that emerged from Emby. It allows you to organize and stream your movies, TV, music, and photos to any client (smartphone, tablet, laptop, smart TV) (Top 10 Media Servers for Home Use in 2023 | ZimaSpace Blog) (Jellyfin: The Free Software Media System). Jellyfin can run on ARM (there are builds for Raspberry Pi) and even do on-the-fly transcoding if the device is powerful enough (though transcoding video on a Pi is limited ā but direct streaming works fine). It has web and mobile clients, making it convenient.
- Plex/Emby: Plex Media Server is popular (though not fully open-source, itās free to use). Itās very polished and could run on our device, but it might try to use cloud features. Emby is similar (closed source freemium). Given the open-source focus, Jellyfin is preferable.
- MiniDLNA/ReadyMedia: If a full media library management is not needed, a simple DLNA server like ReadyMedia can share media files to any DLNA client with minimal resource usage. This is ultra-lightweight but provides no fancy interface.
- Nextcloud: For serving personal files (documents, notes, photos) in a Dropbox-like fashion, Nextcloud is a great open-source server. On our wearable, Nextcloud could be overkill, but a lightweight alternative could be Syncthing (for syncing files between your wearable and other devices without cloud). However, if the use-case includes accessing personal data from the wearable hub, a web interface like Nextcloudās could be useful.
- Security for media access: Since this device might carry personal media and data, ensuring secure access is important. Open-source reverse proxies (like Caddy or Nginx) could be used with TLS if you plan to access the media server from outside your local network. For an always-carried device, local access (via Wi-Fi direct or Bluetooth) might suffice, but one might also allow the wearable to act as a hotspot server to stream to a tablet or glasses in a field scenario.
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System Security Considerations: With personal data and always-on sensors (microphone) on the device, security and privacy are paramount. Open-source gives us transparency, but we must still harden the system:
- Use a minimal set of services and close unnecessary network ports. Configure a firewall (ufw or similar on Linux) to restrict access to only whatās needed (e.g. Jellyfinās port, an SSH for maintenance perhaps).
- Keep the OS updated to patch vulnerabilities.
- Encrypt sensitive data at rest ā for example, the media files might not need encryption, but any logs or AI conversation history could be encrypted or at least protected. If the device is lost, we donāt want personal info easily retrievable. Linux allows disk encryption (LUKS) or at least encrypted containers for sensitive files.
- Since this is a PAN device, it might automatically pair with your devices; using authentication (PIN codes for Bluetooth pairing, strong Wi-Fi passwords) is necessary to prevent unauthorized access.
- Personal AI data (like recorded conversations) should be stored locally and possibly periodically pruned. Owlās project emphasizes privacy ā data is stored on your server and you should inform people if you record them (GitHub - OwlAIProject/Owl: A personal wearable AI that runs locally). Our device should likely implement an easy āclear historyā function for the user.
- If remote access is needed (say you want to query your assistant from your phone while the device is in your backpack), consider running an HTTPS server or using a secure tunnel (VPN or something like
ngrok
) (GitHub - OwlAIProject/Owl: A personal wearable AI that runs locally). This prevents exposing an open API to the outside world without protection. - Finally, open-source security tools like Fail2Ban (to block suspicious login attempts) or SELinux/AppArmor (to confine processes) can be employed if our use-case warrants them.
In terms of open-source firmware, if we incorporate any microcontroller (for power management or sensor hubs), we can use Arduino or Zephyr (open RTOS) to program it. But the main system will rely on standard Linux.
To summarize, the software stack would likely be: Linux OS, running services like Jellyfin (media server), the AI assistant service (custom or Mycroft/etc.), possibly a database (for logs or vector embeddings for AI memory), and standard Linux networking. All of these have open-source solutions. This software stack can be tuned to run on modest hardware ā for example, community members have run Jellyfin on Raspberry Pi and managed local voice assistants on Pi with proper configuration.
5. Budget Analysis (Low-end, Mid-range, High-end Builds)
Cost can vary widely depending on the components chosen. We outline three potential builds ā minimum viable (low cost), an optimized mid-range, and a high-end power user build ā including estimated costs and the value each provides.
A. Minimum Viable Cost Build (Low-End Prototype):
Goal here is to minimize cost while achieving basic functionality of a wearable AI and media server. This will involve some compromises in performance but prove the concept.
- Core SBC: Raspberry Pi Zero 2 W ($15) or Raspberry Pi 4 Model B (2GB model) around $45. The Pi Zero 2 W is extremely small and cheap, with a quad-core Cortex-A53. It can run basic voice assistant tasks (with lightweight models) and act as a simple server, but would struggle with heavier AI. The Pi 4 with 2GB or 4GB RAM is a safer low-end choice for more headroom (RaspberryPi Powered Wearable Computer : 8 Steps (with Pictures) - Instructables) (RaspberryPi Powered Wearable Computer : 8 Steps (with Pictures) - Instructables). If Raspberry Pis are hard to get, alternatives like an Orange Pi 4 LTS ($50) or a used older-model SBC could work. For our cost analysis, assume Raspberry Pi ~ $45.
- Storage: Rely on a microSD card to save cost initially. A 128Ā GB high-speed microSD ā about $20 ā can hold the OS and some media. (The user could later add a USB drive if needed).
- Battery and Power: Use a pre-made USB power bank (much cheaper than custom batteries with BMS). For example, a 10,000 mAh power bank ā about $20. Some power banks can output while charging (pass-through); one might have to test or choose one that is known to support this if needed. Otherwise, one could use 2Ć18650 cells and a cheap charger module (could be slightly cheaper, around $15 total for cells and a board).
- Connectivity: The Pi has Wi-Fi and BT onboard, so no extra cost. If NFC is desired, maybe skip in low-end. Cables and misc (USB cable, some connectors): $10.
- Enclosure/Strap: Could 3D-print a simple case or even use a small camera pouch to attach to belt ā say $0ā10. (In instructables, a $5 cell-phone case was repurposed (RaspberryPi Powered Wearable Computer : 8 Steps (with Pictures) - Instructables)).
- Total Low-End Cost: Roughly $100 (give or take). E.g.: $45 (Pi) + $20 (SD) + $20 (Battery) + $15 (misc cables, case) = $100. If using the Pi Zero route, it could be even lower (~$60 total with Pi Zero, smaller SD, etc.), but performance would be limited. For this ~$100 build, you get a basic wearable that can run a small assistant (maybe keyword commands or very small LLM responses) and serve media (albeit the media storage is limited to ~100GB in this scenario). Itās the most cost-effective path, leveraging commodity parts.
B. Optimized Mid-Range Build (Balanced Performance vs Cost):
This build targets solid performance for a smooth experience while still being budget-conscious. It should comfortably run a decent local AI model and stream media, and be more future-proof than the low-end.
- Core SBC: NVIDIA Jetson Nano Developer Kit (
$99) or Raspberry Pi 4/5 with 4ā8GB ($75). The Jetson Nano (4GB) gives a big AI boost for the price ā as noted, it can be ~3-5Ć faster than Pi4 in AI tasks (Training ANNs on the Raspberry Pi 4 and Jetson Nano), thanks to its 128-core GPU, and itās still relatively low cost. However, it lacks built-in storage and Wi-Fi, so one might add a $10 USB Wi-Fi adapter if needed. The Raspberry Pi 5 (4GB) at around $60 is another compelling option: it has much faster CPU than Pi4 and now supports NVMe SSD (via an addon board ~$20). It doesnāt match Jetson in raw AI, but the Pi 5ās improved speed and potential for NVMe make it balanced. Letās assume $100 for the core compute (covering either choice plus any needed small extras). - AI Accelerator (optional): If using Raspberry Pi, adding a Coral USB Accelerator (
$60) would dramatically improve ML inference (4Ā TOPS). If using Jetson, no external accelerator is needed for this tier. Another option is the Intel Neural Compute Stick 2 ($70) similarly. We might allocate $0ā$70 here depending on whether we want to include this. For a balanced build, letās include one for capability: say +$60 for an Edge TPU. (This could be skipped to save cost, using onboard capabilities only). - Storage: A 250Ā GB NVMe SSD (
$30ā$40) if NVMe is supported (Pi 5 or other board with M.2). For Jetson Nano (which uses microSD by default), one could use a 128Ā GB microSD plus a 256Ā GB USB3 SSD ($40). To keep it simple: budget $40 for solid-state storage (>=250Ā GB), which is plenty for OS, AI models, and many media files. - Battery and Power: Use a higher-quality Li-ion setup. Perhaps 3Ć 18650 cells with a proper buck/boost converter for stable output. For example, three LG 18650 cells (~$5 each) and a charger/protection board ($15), plus a DC-DC converter ($10) for 5Ā V output. Total about $40 for a custom battery pack. This might provide ~30Ā Wh which could run a
5ā7 W system for 4ā5 hours. Alternatively, a ready-made high-output power bank (with PD output) like a 20,000 mAh PD bank ($50). Weāll say $40 for battery components in this tier. - Connectivity & Extras: The mid-range might add a bit more: maybe an NFC module ($5) or a better Bluetooth module if needed. But likely the onboard connectivity suffices. Allocate $20 for miscellaneous (cables, switches, case mounting, perhaps a small OLED display for status).
- Total Mid-Range Cost: Approximately $240 (assuming we include an AI accelerator). Breakdown: $100 SBC + $60 accelerator + $40 SSD + $40 battery + $20 misc. If we dropped the accelerator, itās closer to $180. This build, for example, could be a Jetson Nano ($99) with a 256GB SSD ($35), running on a custom battery pack, all mounted in a small belt-case. It would handle a larger local AI model (Nanoās GPU can run a 7B parameter model inference much faster than a Pi can), and store a sizable media library. Itās a solid balance of capability and cost ā likely the best value for most users looking to experiment seriously with a wearable AI.
C. Cutting-Edge High-End Build (Maximum Capability):
For power users willing to spend more to get the best performance, we can spec a high-end wearable rig. This could enable running larger AI models, doing more intensive tasks, or simply extending runtime and features.
- Core Compute: NVIDIA Jetson Orin NX or Xavier NX module (
$400ā$600 for dev kits), or an Intel NUC 12 Enthusiast board ($450), or an AMD Ryzen embedded like Udoo Bolt V8 (was ~$400). These higher-end boards give desktop-level AI performance. For instance, Jetson Xavier NX delivers 21Ā TOPS and comes with 8ā16GB RAM (NVIDIAĀ® Jetson Xavier⢠NX Module 16 GB - Seeed Studio) ā capable of running mid-size neural nets in real-time. Jetson Orin can go up to 40Ā TOPS on the Nano form factor ([PDF] jetson-orin-nano-datasheet-r4-web.pdf - Open Zeka). An Intel i7 NUC board could even allow running a 13B LLM with optimizations, and has x86 compatibility for various software. However, all of these draw more power (10ā30Ā W typical). Weāll allocate $500 for the computing unit in this tier. - Co-processors: In a high-end build, one might include multiple accelerators. For example, adding a second GPU or a dedicated NPU. But given the constraint of wearability, it might be wiser to stick to one main board. One exception: if using an x86 mini PC that lacks a strong AI chip, adding an accelerator like the Hailo-8 (26Ā TOPS) or multiple Coral TPUs could be considered. Letās earmark $0ā$200 for any co-processor. (E.g., two Coral M.2 TPUs at $60 each could provide ~8Ā TOPS combined).
- Memory & Storage: At this level, weād want ample memory (16Ā GB RAM if possible, for large models or multitasking) and lots of storage for media and model files. A 1Ā TB NVMe SSD (~$100) is appropriate, possibly combined with a second SD or external drive if needed for backups. So around $100 for storage.
- Battery System: To power a 20Ā W system for several hours, a robust battery pack is needed. This might be akin to a laptop battery. For example, a pack of 4-series lithium-ion (14.8Ā V) with ~6600 mAh could provide ~97Ā Wh. Thatās on the order of a large laptop battery, which could run a ~20 W draw for ~5 hours. One could either buy a pre-made high-capacity battery (some drone or RC batteries offer >Ā 90 Wh) for ~$100, plus a suitable regulator. Also consider using two hot-swappable packs (carry a spare to double runtime). For this estimate, allocate $150 for a high-end power solution (cells + smart charger + regulator circuits). This might include an advanced BMS that communicates battery stats, etc.
- Enclosure & Cooling: High-end components generate heat, so a custom enclosure with heatsinks or even a small quiet fan might be needed (especially for something like an Orin NX which can draw 15Ā W sustained ā it has a heatsink/fan typically). The cost here might be materials and perhaps a custom 3D print or laser-cut case ā maybe $50 allocated.
- Additional Peripherals: Possibly include a head-up display or AR glasses interface in the high-end budget (though the question focus is on the core device, not the peripherals). If so, something like Vufine display ($200) or a used Google Glass could be an add-on, but weāll treat that separately. For the core device, we might include a high-speed wireless module (Wi-Fi 6E card, $30) or 5G modem ($100) at this tier for cutting-edge connectivity. Letās say $100 for any such extras.
- Total High-End Cost: Rough estimate ~ $900 ā $1000+. Breakdown: $500 compute + $100 storage + $150 battery + $50 enclosure + $100 extras = $900. It can go higher if very specialized parts are used (for instance, Jetson AGX Orin dev kit is ~$2000 which is beyond even this scope, or if one buys a brand new high-end NUC and mods it). But around $1000 one could build a āwearable computerā that rivals a decent laptop in power. For instance, a Jetson Xavier NX 16GB ($600) with a 1TB NVMe ($100), running off a custom 100Ā Wh battery ($150), mounted in a compact backpack or belt-case. This could likely run a 13B-30B parameter LLM locally (with quantization and the 16GB RAM), handle complex voice and vision tasks, and stream a large personal media library. The user who opts for this build likely wants maximum autonomy and performance ā perhaps an AI that can do intensive tasks on the go (like realtime language translation or running stable diffusion image generation locally, etc.).
Value Considerations: Each budget level offers a different value proposition. The low-end (~$100) achieves the concept with minimal investment ā great for proof-of-concept or for primarily offloading heavy AI to a phone/cloud while the wearable handles lighter tasks. The mid-range (~$200-250) hits the sweet spot, enabling true offline AI functionality with decent responsiveness and enough storage for practical media use ā this would be the recommended starting point for most hobbyists, as it gives a noticeable improvement in capability per dollar spent (Jetson Nano vs Raspberry Pi vs MiniPC : r/ROS - Reddit) (community opinions often favor Pi for cost-effectiveness, unless heavy vision is needed where Jetsonās GPU pays off). The high-end (~$1000) is for enthusiasts or specific professional use-cases; it has diminishing returns in terms of cost/performance ratio but unlocks possibilities that lower tiers canāt, like running larger models or extended operation without recharge. Weāve also chosen components with upgradeability in mind ā e.g., using an SSD instead of soldered storage, using modular SBC or SOM (System on Module) that can be swapped in future, etc., which leads to the next point.
6. Upgradeability Considerations (Modularity and Expansion)
Designing the wearable with future upgrades in mind will extend its lifespan and flexibility. Key areas to consider for modularity are the compute module, storage, battery, and connectivity.
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Modular Compute and Boards: Wherever possible, use standards or socketed modules. For example, Raspberry Piās Compute Module line allows the core SoC/RAM to be on a removable SODIMM-like module that plugs into a carrier board ā a future Compute Module could be slotted in for an upgrade without reworking the entire device. Some high-end SBCs (Jetson, etc.) are already modular (the Jetson is a module that plugs into a dev kit carrier). Designing the wearable around a carrier board means you could upgrade to a newer module (say from Jetson Nano to Jetson Orin NX) if they share pin compatibility or if the carrier supports multiple options. Even if using a non-modular board, ensuring there is space and standard connectors for a different board is useful ā e.g., designing the enclosure to fit a 4ā³Ć4ā³ board means other 4ā³Ć4ā³ SBCs might be swapped in later. Use of USB, HDMI, etc., as external connectors also means a new board can interface without custom wiring as long as it exposes those standards.
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Storage Upgradeability: As mentioned, prefer removable storage. If using microSD, thatās naturally swappable (but consider providing an external microSD slot in the design for easy access). If using SSD, choose standard form factors (M.2 NVMe or 2.5ā³ SATA) and ensure they are accessible (maybe a small door or by removing a few screws). This way, as higher capacity or faster SSDs come down in price, the user can upgrade from, say, 256Ā GB to 1Ā TB easily. In a multi-module design, one might even have multiple storage slots (e.g., one internal SSD and one SD card slot for expansion).
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Battery and Power: Batteries wear out over time (cycle wear), so user-replaceable battery is important. The design could allow the battery pack to be unplugged or unsnapped and replaced. For instance, using an 18650 cell holder instead of fixed soldered cells lets the user swap out cells when they degrade or if they want higher capacity ones (Open Source Battery Pack). If a pouch cell is used, perhaps secure it with a connector and bracket so it can be changed with minimal soldering. Another aspect is supporting different power sources ā maybe today the user carries a Li-ion pack, but in future they might clip on a higher-tech battery. Ensuring the power management circuit can handle a range of input voltages or battery chemistries (with minor config changes) is forward-looking. Some enthusiasts might want to add solar panels or other charging methods later; providing a connector for auxiliary charging (like a DC input jack or a MPPT solar charger input) could be a nice upgrade path.
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Connectivity Upgrades: The wireless standards evolve (Wi-Fi 7, Bluetooth 5.3, etc.), and new networks like UWB or future 6G may become relevant. Designing with modular radios ā for example, the Raspberry Piās onboard Wi-Fi is not swappable, but on a custom build one could use an M.2 or mini-PCIe slot for a Wi-Fi/BT card (common in x86 boards). This would allow upgrading the wireless card later. Similarly, providing a USB or M.2 slot that could host a cellular modem (even if not used initially) gives the user an option to add 5G connectivity later without rebuilding the device. For sensors, using standard interfaces (USB, I2C, SPI exposed via connectors) means new sensor modules (maybe an environmental sensor, or a better microphone array, etc.) can be attached as addons. Essentially, expose the boardās expansion headers in the design (perhaps a small internal USB hub or a break-out of GPIO pins to a header) so that thereās room to grow.
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Physical Modularity: Consider a āstackā design or compartment design. For example, one compartment holds the SBC, another holds the battery, connected by a cable or a docking connector. This way the user could upgrade one part without touching the other. Some DIYers make use of magnetic connectors or docking ports so that, say, the battery module can detach and you can dock a fresh one. While more complex, itās an upgrade path for heavy users. Also, using a tool-less entry (like thumbscrews or clips) to open the case encourages tinkering and upgrading. Contrast this with sealed gadgets ā we want ours to be hobbyist-friendly.
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Software Upgradeability: Though not hardware, making sure the software environment can be updated and expanded easily is crucial. Using standard package managers and containerization (maybe running services in Docker, which can be updated independently) will help the device stay usable long-term. This ties into upgradeability because as new AI models or features come out, the user can install them if the hardware supports it, rather than being locked to a fixed capability. For instance, if a future small LLM runs better on a slightly different accelerator, the modular approach would allow adding that accelerator and installing the new software for it.
Best Practices: To summarize, treat the wearable device not as a sealed gadget but as an ecosystem of modules. Document the internal layout and provide schematics if possible (aligning with open-source principles) so users can modify it. Use non-proprietary connectors ā USB, HDMI, standard battery connectors, etc., rather than something custom thatās hard to adapt. Look at PC design for inspiration: desktop PCs thrive because of standardized components (ATX, PCIe, etc.). On a smaller scale, projects like the Framework laptop (which is built for modular upgrade) show that itās feasible to make high-performance devices upgradeable ā our wearable can do the same by allowing key parts to be swapped as needed. In practical terms, a wearer could start with, say, a Raspberry Pi 4 in the belt pack, and two years later swap in a Pi 5 or an Intel Up-board if they need more power, reusing the rest of the system.
7. Industry Comparisons & Existing Products for Inspiration
Itās helpful to compare our concept with similar devices, both current products and past attempts, to glean insights on design and functionality. A few notable references include the Omi AI medallion, the Humane AI Pin, open projects like Owlās āBeeā device, and earlier wearable computing efforts.
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Omi AI Wearable: Omi is a small AI gadget, about the size of a large coin, that can be stuck to the forehead or worn as a necklace. It acts as a personal AI that listens and responds without wake words (meet omi, a wearable AI device that can read minds) (meet omi, a wearable AI device that can read minds). Omiās approach is to rely on a paired smartphone for heavy processing and internet, while the wearable itself is mostly sensors and a low-power processor. This offloading allows Omi to achieve a 3-day battery life on a tiny device (Omi: An AI Wearable Aiming to Revolutionize Personal Interaction), which is impressive. The takeaway for our device is the benefit of low-power design and perhaps offloading some tasks to a companion device to save battery. However, our aim is local processing, so our power needs are higher. Omi also emphasizes privacy (local storage of conversations that user can delete) and convenience (no wake word needed, itās always listening and smartly filtering) (Omi: An AI Wearable Aiming to Revolutionize Personal Interaction) (Omi: An AI Wearable Aiming to Revolutionize Personal Interaction). We should incorporate a similar hands-free user experience ā perhaps a toggle for always-listening mode with local wake word detection to wake the assistant instantly. Omiās form factor (a forehead sticker or necklace) underscores extreme portability, but it sacrifices upgradeability and compute power. Our device will be larger, but learning from Omi, we should still aim for as small and comfortable as possible for the given performance.
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Humane AI Pin: The Humane Ai Pin is a recent high-profile wearable that clips to the chest and offers an AI assistant with environmental awareness (it has a camera and projector) (Humane Ai Pin Review: Too Clunky, Too Limited | WIRED) (Humane Ai Pin Review: Too Clunky, Too Limited | WIRED). It is cloud-centric, using OpenAI and other cloud models, essentially functioning as a gateway to AI services. Some insights from reviews: it has thermal issues and overheating problems (Humane Ai Pin Review: Too Clunky, Too Limited | WIRED), which caused negative feedback. This highlights the challenge of packing too much into a small form without proper cooling ā something we must watch for in our design if we include higher-power chips. The Pin also had an innovative projector display on the hand, but reviewers found it gimmicky and hard to see in daylight (Humane Ai Pin Review: Too Clunky, Too Limited | WIRED). That suggests that having a good interface (maybe a paired AR glasses or just audio feedback) might be better than an underwhelming micro-projector. Notably, the Ai Pin is very expensive ($699 device + subscription) (Humane Ai Pin Review: Too Clunky, Too Limited | WIRED) (Buy Ai Pin | Humane), and yet scored only 4/10 in a Wired review due to limited features and clunky performance (Humane Ai Pin Review: Too Clunky, Too Limited | WIRED). The lesson for us is that offline functionality and well-chosen features can actually beat flashy but impractical features. Where the Ai Pin struggles (overheating, limited offline use, poor display), our DIY device can choose simpler, proven methods (like using your phone or glasses for display, and focusing on robust voice interaction). Also, the Ai Pinās need for a subscription underlines the advantage of an open self-hosted device ā no recurring costs and user control. One thing the Ai Pin does well is seamless connectivity ā it acts as a standalone cell phone essentially (it has its own number). For our inspiration, if a user wanted, they could insert a 4G/5G dongle and similarly free themselves from needing a phone. But itās optional, which provides flexibility.
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Owl āBeeā Device: The Owl projectās reference hardware called Bee is very relevant. Bee is described as a 1.2-inch diameter device with 50-hour battery life (GitHub - OwlAIProject/Owl: A personal wearable AI that runs locally), meant to capture audio and send to an AI. Itās likely similar in concept to Omi ā small form factor, long battery, but using a companion server (the Owl AI server which can run on a phone or any computer) (GitHub - OwlAIProject/Owl: A personal wearable AI that runs locally) (GitHub - OwlAIProject/Owl: A personal wearable AI that runs locally). The Bee device suggests that splitting the system into two parts (a low-power wearable sensor and a more powerful processor elsewhere) is a common design for maximizing wear time. While our goal is to consolidate into one, we might consider a hybrid approach: e.g., have an ultra-low-power microcontroller always listening, and only wake the main SBC when needed. That way our device can mimic the duty cycling of these small devices. Beeās success in battery life reinforces applying the power management tactics we discussed ( Designing for Low Power in Battery-Operated Devices ā Omi AI) ( Designing for Low Power in Battery-Operated Devices ā Omi AI). The Owl project also uses Apple Watch as a capture device option (GitHub - OwlAIProject/Owl: A personal wearable AI that runs locally) ā which again speaks to leveraging existing wearables for front-end sensing. For upgradeability and openness, Bee is offered to contributors to tinker, aligning with our open philosophy. Essentially, the Owl projectās architecture (distributed capture + central AI server) could be an inspiration if we find our single-unit wearable draining too fast; one could offload some roles to other personal devices if needed.
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Earlier Wearable Computers (Xybernaut, etc.): In the late 90s and early 2000s, companies like Xybernaut and projects like the Rockwell Trekker built fully wearable PCs (Wearable Computing) (Wearable Computing). They typically had a belt-mounted computing unit and head-mounted display, often with voice commands. Those devices were heavy (the Trekker was a 120Ā MHz Pentium and cost $10k (Wearable Computing)) and had very limited battery life, but they proved the concept for industrial use. One interesting product was the Via Pack PC, which was literally a computer in a belt form factor (Wearable Computing). These predecessors show that belt-mounted is proven and that hands-free operation is key. The comfort studies from that era led to guidelines we already cited (keeping weight on waist, not arms) (Where It ā BODY MAPS ā Clint Zeagler) (Where It ā BODY MAPS ā Clint Zeagler). Modern technology lets us vastly shrink what they had, but we should still heed their solutions: for example, many had quick-release connectors for cables (so you could detach the head display or battery easily). For our device, if we imagine connecting to glasses via cable, maybe use a magnetic breakaway connector for safety. And ensure the device is robust ā those older ones were made rugged for field use.
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Magic Leap 2 and AR Packs: The Magic Leap 2, as discussed, uses a compute pack worn on the hip connected by a cable to the headset. It runs a sophisticated AR platform with onboard SLAM (mapping) and an array of sensors. The compute pack gets about 3.5 hours on its internal battery (Battery Life ā Care - Magic Leap), but Magic Leap provides an external battery option to extend that (hot-swappable) (All-Day Smart Glasses | Magic Leap - Expand Reality). This product highlights thermal and weight distribution: they moved processing off the head to the hip pack, which aligns with our thinking. It also shows that even with a large budget and engineering, high-performance wearables often cap out at a few hours battery ā so our expectations for an all-day AI might need to include either lower-power modes or external battery modules. One design insight: Magic Leapās compute pack has a status LED array and only one on/off button, and everything else is controlled via the software UI in the glasses. Our device could similarly minimize physical controls to just an essential few (maybe a power switch and a manual override to mute the mic for privacy), relying on voice or a paired phone for other settings.
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Smartphones and Pocket Computers: Lest we forget, the most successful āwearableā computer is arguably the smartphone (usually pocket carried). A modern smartphone has significant AI capability (some have NPUs that can do AI tasks offline), and can act as a media server or assistant, but they are general-purpose and not open-source. One could ask: why not just use a smartphone? The answer for our scenario is control and openness ā we want to tinker, upgrade, and run truly personal, local AI. However, learning from smartphones is useful: they heavily optimize power (chips go into ultra-low power states when idle, etc.), they use custom ASICs for things like video encoding to save power. We might not have those advantages, but we can try to emulate the always-on readiness of phones by smart power use. Also, phones set user expectations: instant responses, smooth UIs, etc. Our device will need to manage user expectations if it canāt answer as quickly as, say, Siri. Possibly framing it as an advanced āmini-serverā that complements the phone (rather than replaces it) could be wise initially.
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Other Niche Devices: There are also devices like the Nreal / Xreal Air glasses (which require a compute source like a phone or a small adapter) and older things like the Palm Foleo (mobile companion). All these indicate a trend of splitting heavy compute from user interface in wearables. The Amazon Alexa Echo Frames (audio glasses) and Echo Loop (smart ring) show attempts to integrate assistants into wearables, but they rely on phone/cloud. Our fully local approach is closer to devices like the Cyrcadia wearables or OpenAIās one-off demos, but really itās a unique proposition.
Key Insights and Recommendations Drawn:
From these comparisons, a few key points stand out:
- Always-On & Low-Power: The best user experience comes from devices that are always ready (no boot-up delay, no waking up manually). Achieving that means mastering low-power operation. Implement a tiered processing design (small always-on logic, big processor sleeps when not needed) to mimic devices like Omi and Bee which achieve multi-day life.
- Wearability & Comfort: Devices that failed often were too bulky or got hot (Ai Pin overheating, Google Glassās infamous heat and battery issues). So prioritize thermal solutions (heat sinks, maybe using the metal case as a heat spreader) and keep the unit slim enough not to snag on things. A ventilated enclosure or using the metal belt clip as a heat dissipation surface could be ideas.
- Privacy and Trust: Many users are wary of always-listening devices (Google Glass faced backlash, Ai Pin might too). Our open-source approach should be transparent about whatās recorded and allow easy control (like a physical mute switch for mic, or LED indicator when recording). Emphasize the local-only nature as a selling point ā no one else is hearing your data, unlike cloud services.
- Modularity vs Size: Industry devices go for sleek integrated designs (sealed units) to look nice, but we value modularity. There is a middle ground perhaps ā e.g., Omi is sealed, but our prototype can be slightly larger to allow swapping parts. Since this is targeted to enthusiasts initially, a slightly utilitarian look (like a tiny āwearable PCā with visible screws) is acceptable. Over time, one could iterate to make a more polished version once the component choices stabilize.
- User Interface: Most of these devices either rely on a phone app or voice feedback. For our prototype, we should definitely have a companion app or at least a web interface for configuration and maybe showing rich outputs (like if the user asks a complex question, maybe the answer is better viewed on their phone or glasses). Omi uses a phone app extensively (meet omi, a wearable AI device that can read minds). The Ai Pin attempts to use projection which didnāt pan out well. So a simpler approach is to use existing screens (phone, tablet, AR glasses) as needed, via Wi-Fi or BT.
- Functionality Focus: It's easy to get feature creep. The Ai Pin tried camera, projector, voice, calls, etc., and ended up mediocre in each (Humane Ai Pin Review: Too Clunky, Too Limited | WIRED). A better approach might be to focus on a couple of killer features ā in our case, a truly personal AI (that can answer without internet) and a personal media hub. Other things like being a full phone or AR computer can be secondary or left to other devices. Ensuring those two primary functions work reliably will make the device valuable.
Finally, itās inspiring to see communities (like r/cyberDeck or wearable PC builders) making their own devices (Wearable Tech/Pip-Boy Inspired build Discussion : r/cyberDeck). Our approach aligns with that DIY spirit but with a specific application in mind. By learning from both the successes and mistakes of the above examples, we can craft a prototype that stands on the shoulders of giants ā combining the portability of Omi, the capability of a Jetson/Pi, and the user-control and upgradeability that commercial products donāt offer.
Conclusion and Recommendations
After this in-depth analysis, the recommended path is to develop a waist-mounted wearable AI server as the prototype form factor, using a capable yet power-conscious SBC, powered by a robust Li-ion battery pack, and running open-source software for AI and media serving. A Raspberry Pi or Jetson-based build in a belt-pack appears to best meet the guiding principles of efficiency, cost-effectiveness, and ease of use. Key hardware components include at least 4ā8GB of RAM, fast solid-state storage (leveraging new Pi 5 NVMe support for ~10Ć speed over microSD (Pimoroni NVMe Base for Raspberry Pi 5 Review: NVMe for Me | Tom's Hardware)), and integrated wireless connectivity (Wi-Fi 5/6, Bluetooth 5) for personal area networking. Where budget allows, adding an AI accelerator (like an Edge TPU or using a Jetsonās GPU) will significantly boost on-device AI responsiveness ā beneficial for running local voice recognition and language models.
For power, a custom lithium battery solution with around 30ā50Ā Wh capacity (e.g. a pack of 18650 cells on a buck/boost regulator) is advised, which in testing should yield several hours of active use and much more in idle or low-power mode. Employ advanced power management: utilize sleep states and ultra-low-power mic listening to extend standby time towards all-day use. The software stack should comprise a lean Linux OS with services like Jellyfin for media and a voice assistant stack (possible combination of Mycroft for voice interface with a local LLM backend). This ensures the device can operate fully offline, aligning with the ethos of personal control. Budget-wise, one can start with the mid-range build (~$200-$300) which is achievable for hobbyists and offers a strong foundation, then upgrade components (bigger battery, more powerful SBC) over time as needed.
Crucially, the design must remain modular and user-upgradable. All major parts ā compute board, storage drive, battery, and wireless modules ā should be accessible and replaceable. This will not only extend the deviceās lifespan but also foster a community of builders who can customize the wearable to their needs. By following standards and including expansion interfaces, we future-proof the device against new developments. In effect, we are proposing an āopen-source wearable computing platformā rather than a single gadget, much like how early personal computers invited tinkering and upgrades.
In summary, the convergence of compact hardware and open-source AI software makes it feasible to create a wearable PAN device that serves as oneās personal AI and media hub. By learning from existing products (avoiding their pitfalls like short battery life and overheating (Humane Ai Pin Review: Too Clunky, Too Limited | WIRED)) and leveraging the wealth of open-source projects (from Leon and Mycroft for assistants to Jellyfin for media), we can assemble a prototype that is personal, private, and powerful. With a belt-worn module housing a Raspberry Pi/Jetson, a swappable battery, and modular expansion, users would have their own Jarvis-like assistant and media server on the go ā completely under their control. As components continue to improve (imagine future graphene batteries (All Kinds of Wearable Batteries Comparison) or even more efficient AI chips), the modular foundation will allow this wearable platform to evolve. The first prototype is just the beginning, but with the recommendations laid out above, we have a clear roadmap to building it in a way that is practical, versatile, and aligned with the DIY maker spirit.
Sources:
- Zeagler, C. ā Design for Wearability & Body Maps ā guidelines on placing weight on the body (Where It ā BODY MAPS ā Clint Zeagler) (Where It ā BODY MAPS ā Clint Zeagler).
- NVIDIA Developer Blog ā Jetson Xavier NX Specs ā AI performance up to 21 TOPS at ~10Ā W (NVIDIAĀ® Jetson Xavier⢠NX Module 16 GB - Seeed Studio).
- RARE School Blog ā Jetson Nano vs Raspberry Pi 4 Training Benchmarks ā Jetson 2.8ā5.7Ć faster than Pi4 in CNN training (Training ANNs on the Raspberry Pi 4 and Jetson Nano).
- Tomās Hardware ā Raspberry Pi 5 NVMe SSD Performance ā NVMe read ~876Ā MB/s vs microSD 90Ā MB/s (Pimoroni NVMe Base for Raspberry Pi 5 Review: NVMe for Me | Tom's Hardware).
- AI News ā Omi AI Wearable Details ā Always-listening pendant with 3-day battery (Omi: An AI Wearable Aiming to Revolutionize Personal Interaction) and AI summarization features (Omi: An AI Wearable Aiming to Revolutionize Personal Interaction) (Omi: An AI Wearable Aiming to Revolutionize Personal Interaction).
- Wired ā Humane AI Pin Review ā Notes on overheating and limited functionality (4/10) (Humane Ai Pin Review: Too Clunky, Too Limited | WIRED) (Humane Ai Pin Review: Too Clunky, Too Limited | WIRED).
- Owl AI (GitHub) ā Owl Project README ā Vision for always-on wearable AI and āBeeā device specs (1.2ā³ size, 50h battery) (GitHub - OwlAIProject/Owl: A personal wearable AI that runs locally).
- Instructables ā Raspberry Pi Wearable Computer ā DIY example with Raspberry Pi on belt clip and battery pack (RaspberryPi Powered Wearable Computer : 8 Steps (with Pictures) - Instructables) (RaspberryPi Powered Wearable Computer : 8 Steps (with Pictures) - Instructables).
- Grepow Batteries ā Wearable Battery Types ā Graphene batteries have highest energy density among current types (All Kinds of Wearable Batteries Comparison).
- Clint Zeagler ā Wearable Tech Body Maps ā Recommends heavy components at waist, not extremities (Where It ā BODY MAPS ā Clint Zeagler).
Besides, deterioration of media and preservation of data is a serious consideration as we move more and more into the Intelligence Age.
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