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Piezoelectric energy harvester, which generates electricity from stress orvibrations, is gaining increasing attention as a viable solution to extendbattery life in wearables. Recent research further reveals that, besidesgenerating energy, PEH can also serve as a passive sensor to detect human gaitpower-efficiently because its stress or vibration patterns are significantlyinfluenced by the gait. However, as PEHs are not designed for precisemeasurement of motion, achievable gait recognition accuracy remains low withconventional classification algorithms. The accuracy deteriorates further whenthe generated electricity is stored simultaneously. To classify gait reliablywhile simultaneously storing generated energy, we make two distinctcontributions. First, we propose a preprocessing algorithm to filter out theeffect of energy storage on PEH electricity signal. Second, we propose a longshort-term memory (LSTM) network-based classifier to accurately capturetemporal information in gait-induced electricity generation. We prototype theproposed gait recognition architecture in the form factor of an insole andevaluate its gait recognition as well as energy harvesting performance with 20subjects. Our results show that the proposed architecture detects human gaitwith 12% higher recall and harvests up to 127% more energy while consuming 38%less power compared to the state-of-the-art.

Simultaneous Energy Harvesting and Gait Recognition using Piezoelectric Energy Harvester