Capacitor-based activity sensing for kinetic-powered wearable IoTs

• Main Authors: LAN, Guohao, MA, Dong, XU, Weitao, HASSAN, Mahbub, HU, Wen
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2020
• Subjects: Kinetic energy harvesting; Capacitor; Activity recognition; Wearable IoTs; Software Engineering
• Online Access: https://ink.library.smu.edu.sg/sis_research/6839; https://ink.library.smu.edu.sg/context/sis_research/article/7842/viewcontent/Capacitor_basedIoTs_2020_av.pdf
• Institution: Singapore Management University

We propose the use of the conventional energy storage component, i.e., capacitor, in the kinetic-powered wearable IoTs as the sensor to detect human activities. Since activities accumulate energy in the capacitor at different rates, the charging rate of the capacitor can be used to detect the activities. The key advantage of the proposed capacitor-based activity sensing mechanism, called CapSense, is that it obviates the need for sampling the motion signal at a high rate, and thus, significantly reduces power consumption of the wearable device. The challenge we face is that capacitors are inherently non-linear energy accumulators, which leads to significant variations in the charging rates. We solve this problem by jointly configuring the parameters of the capacitor and the associated energy harvesting circuits, which allows us to operate in the charging cycles that are approximately linear. We design and implement a kinetic-powered shoe and conduct experiments with 10 subjects. Our results show that CapSense can classify five different daily activities with 95% accuracy while consuming 57% less system power compared to conventional motion-sensor-based approaches.