Energy-efficient human activity recognition for self-powered wearable devices

Advances in energy harvesting hardware have created an opportunity for realizing self-powered wearables for continuous and pervasive human activity recognition (HAR). Unfortunately, the power requirements of the continuous activity sensing using accelerometer sensors and the burdensome on-node classification are relatively high compared to the amount of power that can be practically harvested, which limit the energy harvesting's usefulness. This thesis proposes a novel paradigm for HAR, which employs kinetic energy harvesting (KEH) and infers human activities directly from the KEH patterns. This novel approach guarantees energy neutrality by eliminating the need for powering accelerometer and reducing the on-node classification overhead, moving us closer towards self-powered autonomous activity monitoring wearables.