TY - GEN
T1 - Energy-Optimal Gesture Recognition using Self-Powered Wearable Devices
AU - Park, Jaehyun
AU - Bhat, Ganapati
AU - Geyik, Cemil S.
AU - Lee, Hyung Gyu
AU - Ogras, Umit
N1 - Funding Information:
Acknowledgment: This work was supported partially by NSF CAREER Award CNS-1651624, and NRF grant funded by the Korea government (MSIT) (NRF-2017R1D1A1B03032382 and 2018R1C1B5047150).
Funding Information:
This work was supported partially by NSF CAREER Award CNS-1651624, and NRF grant funded by the Korea government (MSIT) (NRF-2017R1D1A1B03032382 and 2018R1C1B5047150).
Publisher Copyright:
© 2018 IEEE.
PY - 2018/12/20
Y1 - 2018/12/20
N2 - Small form factor and low-cost wearable devices enable a variety of applications including gesture recognition, health monitoring, and activity tracking. Energy harvesting and optimal energy management are critical for the adoption of these devices, since they are severely constrained by battery capacity. This paper considers optimal gesture recognition using self-powered devices. We propose an approach to maximize the number of gestures that can be recognized under energy budget and accuracy constraints. We construct a computationally efficient optimization algorithm with the help of analytical models derived using the energy consumption breakdown of a wearable device. Our empirical evaluations demonstrate up to 2.4 x increase in the number of recognized gestures compared to a manually optimized solution.
AB - Small form factor and low-cost wearable devices enable a variety of applications including gesture recognition, health monitoring, and activity tracking. Energy harvesting and optimal energy management are critical for the adoption of these devices, since they are severely constrained by battery capacity. This paper considers optimal gesture recognition using self-powered devices. We propose an approach to maximize the number of gestures that can be recognized under energy budget and accuracy constraints. We construct a computationally efficient optimization algorithm with the help of analytical models derived using the energy consumption breakdown of a wearable device. Our empirical evaluations demonstrate up to 2.4 x increase in the number of recognized gestures compared to a manually optimized solution.
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U2 - 10.1109/BIOCAS.2018.8584746
DO - 10.1109/BIOCAS.2018.8584746
M3 - Conference contribution
AN - SCOPUS:85060898220
T3 - 2018 IEEE Biomedical Circuits and Systems Conference, BioCAS 2018 - Proceedings
BT - 2018 IEEE Biomedical Circuits and Systems Conference, BioCAS 2018 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2018 IEEE Biomedical Circuits and Systems Conference, BioCAS 2018
Y2 - 17 October 2018 through 19 October 2018
ER -