  Piezoelectric energy harvester, which generates electricity from stress or
vibrations, is gaining increasing attention as a viable solution to extend
battery life in wearables. Recent research further reveals that, besides
generating energy, PEH can also serve as a passive sensor to detect human gait
power-efficiently because its stress or vibration patterns are significantly
influenced by the gait. However, as PEHs are not designed for precise
measurement of motion, achievable gait recognition accuracy remains low with
conventional classification algorithms. The accuracy deteriorates further when
the generated electricity is stored simultaneously. To classify gait reliably
while simultaneously storing generated energy, we make two distinct
contributions. First, we propose a preprocessing algorithm to filter out the
effect of energy storage on PEH electricity signal. Second, we propose a long
short-term memory (LSTM) network-based classifier to accurately capture
temporal information in gait-induced electricity generation. We prototype the
proposed gait recognition architecture in the form factor of an insole and
evaluate its gait recognition as well as energy harvesting performance with 20
subjects. Our results show that the proposed architecture detects human gait
with 12% higher recall and harvests up to 127% more energy while consuming 38%
less power compared to the state-of-the-art.
