Capacitor-based Activity Sensing for Kinetic-powered Wearable IoTs

Abstract

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.

A Survey of Human Gait-Based Artificial Intelligence Applications

“…Other considerations in carefully selecting the input features are avoiding overfitting ( Zhang et al, 2020a ), improving interpretability, especially in medical applications ( Dindorf et al, 2020b ), ( Horst et al, 2019 ), reducing the energy expenditure of wearable sensors ( Lan et al, 2020 ), ( Russell et al, 2021 ), improving patient comfort ( Di Nardo et al, 2020 ), fairness to people with disabilities ( Trewin et al, 2019 ) and user experience ( Kim et al, 2020 ). To achieve the highest possible accuracy, usually, more complex sensing technology is required.…”

“…CapSense sensing technology uses KEH capacitor voltage traces to recognize among five different activities with an accuracy of over 90% ( Lan et al, 2017 ). In subsequent work, the accuracy was improved by using two capacitors, one in the sole of a shoe and one in front ( Lan et al, 2020 ). The overall system energy usage was also reduced by 75% since the conventional machine learning algorithms such as NB or KNN used accumulated voltage data as input, reducing the computational load of the system.…”

“…To accommodate these requirements, the main focus of the research is on sensing technology. Sensors for wearables are IMUs, capacitive sensors ( Lan et al, 2020 ), KEH ( Xu et al, 2017 ; Xu et al, 2018 ), ( Ma et al, 2018 ), solar cells ( Sandhu et al, 2021 ), resistive sensors, stretchable conductive micro fluids ( Low et al, 2020 ), and TENGs ( Zhang et al, 2020g ).…”

ZEL: Net-Zero-Energy Lifelogging System using Heterogeneous Energy Harvesters

“…However, that system did not have a harvesting function and thus required an external battery. In another study, Lan et al proposed CapSense [7], [8], which connects KEH to a capacitor and recognizes human activities based on its charging rate. Then, using a wearable device with CapSense embedded in shoes, they demonstrated that their proposed system could achieve a 95% accuracy level in recognizing five human activities while consuming 57% less power than conventional systems.…”

“…• We designed and implemented the ZEL net-zero-energy lifelogging system that uses heterogeneous energy harvesters to improve context recognition. [5] Piezoelectric -Walking/running 2017 [6] Piezoelectric -5-activity 2017 [7], [8] Piezoelectric -5-activity 2018 [9], [10] Piezoelectric -Walking detection 2019 [11] Radio frequency 3D position -2019 [12] Solar cell, Piezoelectric 9-place -2019 [13] Solar cell 8-place -2020 [14] Piezoelectric 6-transport mode -2021 [15] Solar cell -5-activity 2021 ZEL Solar cell, Piezoelectric 8-place Static/dynamic (weighted F-measure) of 87.2% and a static/dynamic activities binary classification accuracy level (weighted F-measure) of 93.1%. • By scheduling the system state using a dual power switching mechanism and two comparators, our proposed system was confirmed to work semi-permanently and achieve a 99.6% zero-energy operation level in practical testing.…”