TY - GEN
T1 - ZEL
T2 - 20th IEEE International Conference on Pervasive Computing and Communications, PerCom 2022
AU - Arita, Mitsuru
AU - Nakamura, Yugo
AU - Ishida, Shigemi
AU - Arakawa, Yutaka
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - We present ZEL, the first net-zero-energy lifelogging system that allows office workers to collect semi-permanent records of when, where, and what activities they perform on company premises. ZEL achieves high accuracy lifelogging by using heterogeneous energy harvesters with different characteristics. The system is based on a 192-gram nametag-shaped wearable device worn by each employee that is equipped with two comparators to enable seamless switching between system states, thereby minimizing the battery usage and enabling net-zero-energy, semi-permanent data collection. To demonstrate the effectiveness of our system, we conducted data collection experiments with 11 participants in a practical environment and found that the person-dependent (PD) model achieves an 8-place recognition accuracy level of 87.2% (weighted F-measure) and a static/dynamic activities recognition accuracy level of 93.1% (weighted F-measure). Additional testing confirmed the practical long-Term operability of the system and showed it could achieve a zero-energy operation rate of 99.6% i.e., net-zero-energy operation.
AB - We present ZEL, the first net-zero-energy lifelogging system that allows office workers to collect semi-permanent records of when, where, and what activities they perform on company premises. ZEL achieves high accuracy lifelogging by using heterogeneous energy harvesters with different characteristics. The system is based on a 192-gram nametag-shaped wearable device worn by each employee that is equipped with two comparators to enable seamless switching between system states, thereby minimizing the battery usage and enabling net-zero-energy, semi-permanent data collection. To demonstrate the effectiveness of our system, we conducted data collection experiments with 11 participants in a practical environment and found that the person-dependent (PD) model achieves an 8-place recognition accuracy level of 87.2% (weighted F-measure) and a static/dynamic activities recognition accuracy level of 93.1% (weighted F-measure). Additional testing confirmed the practical long-Term operability of the system and showed it could achieve a zero-energy operation rate of 99.6% i.e., net-zero-energy operation.
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U2 - 10.1109/PerCom53586.2022.9762376
DO - 10.1109/PerCom53586.2022.9762376
M3 - Conference contribution
AN - SCOPUS:85129980224
T3 - 2022 IEEE International Conference on Pervasive Computing and Communications, PerCom 2022
SP - 172
EP - 179
BT - 2022 IEEE International Conference on Pervasive Computing and Communications, PerCom 2022
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 21 March 2022 through 25 March 2022
ER -