Publication Type
Journal Article
Version
acceptedVersion
Publication Date
6-2022
Abstract
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.
Keywords
Piezoelectric Energy Harvesting, Simultaneous Energy Harvesting and Sensing, Gait Recognition, Deep Learning, LSTM
Discipline
Artificial Intelligence and Robotics | Databases and Information Systems
Research Areas
Intelligent Systems and Optimization
Publication
IEEE Transactions on Mobile Computing
Volume
21
Issue
6
First Page
2198
Last Page
2209
ISSN
1536-1233
Identifier
10.1109/tmc.2020.3035045
Publisher
IEEE
Citation
MA, Dong; LAN, Guohao; XU, Weitao; HASSAN, Mahbub; and HU, Wen.
Simultaneous energy harvesting and gait recognition using piezoelectric energy harvester. (2022). IEEE Transactions on Mobile Computing. 21, (6), 2198-2209.
Available at: https://ink.library.smu.edu.sg/sis_research/7008
Copyright Owner and License
Authors
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
https://doi.org/10.1109/tmc.2020.3035045