Capacitor Based Activity Sensing for Kinetic Powered Wearable IoTs
This addresses power efficiency for wearable IoT users, though it is incremental as it builds on existing kinetic energy harvesting and activity sensing concepts.
The paper tackles the problem of power consumption in wearable IoT devices by using a capacitor as a sensor to detect human activities based on charging rates, achieving 95% accuracy in classifying five activities and reducing system power by 73% compared to conventional methods.
We propose a novel use of the conventional energy storage component, i.e., capacitor, in kinetic-powered wearable IoTs as a sensor to detect human activities. Since different activities accumulate energies in the capacitor at different rates, these activities can be detected directly by observing the charging rate of the capacitor. The key advantage of the proposed capacitor based activity sensing mechanism, called CapSense, is that it obviates the need for sampling the motion signal during the activity detection period thus significantly saving power consumption of the wearable device. A challenge we face is that capacitors are inherently non-linear energy accumulators, which, even for the same activity, leads to significant variations in charging rates at different times depending on the current charge level of the capacitor. We solve this problem by jointly configuring the parameters of the capacitor and the associated energy harvesting circuits, which allows us to operate on charging cycles that are approximately linear. We design and implement a kinetic-powered shoe sole and conduct experiments with 10 subjects. Our results show that CapSense can classify five different daily activities with 95% accuracy while consuming 73% less system power compared to conventional motion signal based activity detection.