ArticleScience advances2026
A biomimetic, ultralow-power edge-AI-empowered and self-sustaining gait analysis system.
Article in Science advances, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
14 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.