Human Battery Wristband
A wearable concept that converts raw biosignals into a single 0–100 “battery” score people can actually understand and trust.
Overview
The Human Battery Wristband is a general wellness wearable designed to answer a simple question: “How charged am I right now?” Instead of dumping disconnected metrics (HR, steps, sleep, etc.) on the user, it translates sensor patterns into meaningful states (CHILL / STRESS / WORKOUT / SLEEP) and updates a 0–100 battery score over time using recovery and fatigue rates.
Current status: I have the physical design direction, UI layout concepts, and the planned modular algorithm structure. Full electronics testing and real sensor-driven algorithm validation are still in progress (breadboard + algorithm implementation are the next major steps).
Media
Tech Stack
Key Features
- Single battery score (0–100) that rises with recovery and drops with fatigue over time
- Modular algorithm pipeline: sensors → normalize → flags → state → rates → battery update (per minute)
- Human-readable states (CHILL / STRESS / WORKOUT / SLEEP) so the number has meaning
- Explainability built into the UI concept: show the “why” behind changes (Recovery +__ / Fatigue −__)
My Contribution
- Defined the core problem: wearables show lots of numbers but don’t translate them into an easy “recovery vs fatigue” signal
- Designed the modular scoring architecture so it can be tuned, tested, and explained (instead of one giant mystery equation)
- Planned the state model (CHILL / STRESS / WORKOUT / SLEEP) to map sensor patterns to understandable behavior
- Created UI layout concepts focused on clarity: big battery score first, state label, and details one tap away
- Collected early user feedback and translated it into concrete revisions (slimmer device, simpler home screen, clearer explanations)
Results
- Stronger concept clarity: shifting from raw values to flags + states made the score explainable and less “random math”
- Better buildability: breaking the model into steps makes it easier to implement, debug, and test later
- User trust focus: UI concept now includes explicit reasoning for score changes, not just a number
- Current limitation: electronics + real-world algorithm testing are not complete yet (breadboard + sensor data validation pending)
- Next steps: complete breadboard setup, implement algorithm, run real sensor sessions (sleep, workouts, stress), tune thresholds/rates, and refine physical thickness