Human Context Detection From Kinetic Energy Harvesting Wearables
Sara Khalifa, Guohao Lan, Mahbub Hassan, Wen Song Hu, Aruna Prasad Seneviratne · Advances in wireless technologies and telecommunication book series · 2017
Advances in energy harvesting hardware have created an opportunity for realizing self-powered wearables for continuous and pervasive Human Context Detection (HCD). Unfortunately, the power consumption of the continuous context sensing using accelerometer is relatively high compared to the amount of power that can be harvested practically, which limits the usefulness of energy harvesting. This chapter employs and infers HCD directly from the Kinetic Energy Harvesting (KEH) patterns generated from a wearable device that harvests kinetic energy to power itself. This proposal eliminates the need for accelerometer, making HCD practical for self-powered devices. The authors discuss in more details the use of KEH patterns as an energy efficient source of information for five main applications, human activity recognition, step detection, calorie expenditure estimation, hotword detection, and transport mode detection. This confirms the potential sensing capabilities of KEH for a wide range of wearable applications, moving us closer towards self-powered autonomous wearables.