Simultaneous Energy Harvesting and Gait Recognition Using Piezoelectric Energy Harvester

Piezoelectric energy harvester (PEH), which generates electricity from stress or vibrations, is attracting tremendous attention as a viable solution to extend battery life of wearable devices. More interestingly, besides the energy harvesting capability, recent research has demonstrated the feasibility of leveraging PEH as an power-free sensor for gait recognition as its stress or vibration patters are significantly influenced by the gait. However, as PEHs are not designed for precise motion sensing, the gait recognition accuracy remains low with conventional classification algorithms. The accuracy deteriorates further when the generated electricity is stored simultaneously. In this work, to achieve high performance gait recognition and efficient energy harvesting at the same time, we make two distinct contributions. First, we propose a preprocessing algorithm to filter out the effect of energy storage on PEH electricity signals. Second, we propose long short-term memory (LSTM) network-based classifiers 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 percent higher recall and harvests up to 127 percent more energy while consuming 38 percent less power compared to the state-of-the-art.