TY - GEN
T1 - CapSense
T2 - 14th EAI International Conference on Mobile and Ubiquitous Systems (MobiQuitous 2017)
AU - Lan, Guohao
AU - Ma, Dong
AU - Xu, Weitao
AU - Hassan, Mahbub
AU - Hu, Wen
PY - 2017/11
Y1 - 2017/11
N2 - We propose a new activity sensing method, CapSense, which detects activities of daily living (ADL) by sampling the voltage of the kinetic energy harvesting (KEH) capacitor at an ultra low sampling rate. Unlike conventional sensors that generate only instantaneous motion information of the subject, KEH capacitors accumulate and store human generated energy over time. Given that humans produce kinetic energy at distinct rates for dierent ADL, the KEH capacitor can be sampled only once in a while to observe the energy generation rate and identify the current activity. us, with CapSense, it is possible to avoid collecting time series motion data at high frequency, which promises signicant power saving for the sensing device. We prototype a shoe-mounted KEH-powered wearable device and conduct experiments with 10 subjects for detecting 5 dierent activities. Our results show that compared to the existing time-series-based activity recognition, CapSense reduces sampling-induced power consumption by 99% and the overall system power, aer considering wireless transmissions, by 75%. CapSense recognizes activities with up to 90%.
AB - We propose a new activity sensing method, CapSense, which detects activities of daily living (ADL) by sampling the voltage of the kinetic energy harvesting (KEH) capacitor at an ultra low sampling rate. Unlike conventional sensors that generate only instantaneous motion information of the subject, KEH capacitors accumulate and store human generated energy over time. Given that humans produce kinetic energy at distinct rates for dierent ADL, the KEH capacitor can be sampled only once in a while to observe the energy generation rate and identify the current activity. us, with CapSense, it is possible to avoid collecting time series motion data at high frequency, which promises signicant power saving for the sensing device. We prototype a shoe-mounted KEH-powered wearable device and conduct experiments with 10 subjects for detecting 5 dierent activities. Our results show that compared to the existing time-series-based activity recognition, CapSense reduces sampling-induced power consumption by 99% and the overall system power, aer considering wireless transmissions, by 75%. CapSense recognizes activities with up to 90%.
KW - Activity Recognition
KW - Energy-eciency
KW - Wearable Device
UR - http://www.scopus.com/inward/record.url?scp=85048491333&partnerID=8YFLogxK
UR - https://www.scopus.com/record/pubmetrics.uri?eid=2-s2.0-85048491333&origin=recordpage
U2 - 10.1145/3144457.3144459
DO - 10.1145/3144457.3144459
M3 - RGC 32 - Refereed conference paper (with host publication)
AN - SCOPUS:85048491333
SN - 9781450353687
T3 - ACM International Conference Proceeding Series
SP - 106
EP - 115
BT - Proceedings of the 14th EAI International Conference on Mobile and Ubiquitous Systems
PB - Association for Computing Machinery
Y2 - 7 November 2017 through 10 November 2017
ER -