A Bayesian framework for energy-neutral activity monitoring with self-powered wearable sensors

Achieving energy-efficiency is a challenging task in human activity monitoring. The continuous activity sensing using accelerometer and the burdensome on-node classification rapidly deplete the limited battery resource of the wearable nodes. To reduce the energy overhead and achieve the system energy-neutrality, we present a novel Bayesian framework for human activity monitoring using the energy-harvesting wearable sensors. The proposed framework utilizes a capacitor to store the harvested kinetic energy and uses all the stored energy to transmit an unmodulated signal, called an activity pulse. Our framework can infer the human activity directly from the received signal strength of the activity pulse at a remote server. Neither accelerometer nor classifier is required on the wearable devices, and therefore, our framework guarantees the system energy-neutrality. Using a real dataset collected from a kinetic energy harvester coupled with a Bluetooth prototype, an overall accuracy of 91% is achieved when the distance between the transmitter and the receiver is set to 30 cm.