A biomimetic, ultralow-power edge-AI-empowered and self-sustaining gait analysis system
Creators
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1.
Rutgers, The State University of New Jersey
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2.
University of California, Merced
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3.
New York University
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4.
University of Pennsylvania
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5.
University of Chicago
Description
Smart digital health has reshaped patient monitoring, but it faces a fundamental trade-off between device intelligence and continuous, energy-efficient monitoring. Inspired by self-sustaining intelligent biospecies, we develop a biomimetic, battery-free, and high-precision edge-AI system through a harvested-energy-constrained holistic co-design that couples ultralow-power edge-AI-empowered sensor hardware with biomechanical energy harvesting and cold-start power management. Our edge-AI-empowered motion sensor performs instantaneous, context-aware on-device inference and timely result updating from raw sensor data while consuming only 86 μW. A high-output energy harvester and tailored high-efficiency power management circuitry sustain energy levels exceeding system requirements, eliminating downtime associated with charging and enabling true 24/7, hassle-free monitoring. This breakthrough establishes a paradigm for system-level, edge-AI-empowered, and self-sustaining sensing, demonstrating that intelligence and energy autonomy can coexist within a single wearable platform and pointing to next-generation always-on, personalized digital health systems.
Data availability
All data and code needed to evaluate and reproduce the results in the paper are present in the paper and/or the Supplementary Materials. This study did not generate new materials.
Files
sciadv.aeh9625.pdf
Files
(109.4 MB)
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Additional details
Funding
- New Jersey Commission on Science, Innovation and Technology
- Brain Injury Research Grant CBIR25IRG024
- New Jersey Commission on Science, Innovation and Technology
- Spinal Cord Injury Research Grant CSCR26IRG008
- Rutgers, The State University of New Jersey
- Startup Package
- New Jersey Health Foundation
- PC 221-25