Activity recognition through intermittent distributed processing by energy harvesting PIR sensors

Abstract

As an increasing demand for human activity monitoring in many smart services such as elderly monitoring, there is a keen need of an activities of daily living (ADL) recognition system. which can be easily deployed in ordinary homes and does not require periodic maintenance such as battery replacement for long time. In this paper, we propose an ADL recognition system which can run continuously without feeding power from outlets by intermittent sensing and distributed processing of energy harvesting (EH) sensor modules. Specifically, we have designed and developed an EH sensor node composed of (i) a micro-controller board with an analog PIR sensor which senses human activity as analog signals and form a BLE mesh network with other sensor nodes and (ii) an energy harvest module with solar panels and a rechargeable battery. We have also implemented a simple distributed random forest (RF) classifier consisting of multiple RF classifiers trained independently and running on different nodes which exchange the classification results with each other via BLE and make a final decision based on majority vote. Through experiments with five sensor nodes deployed in our smart home testbed, the distributed RF classifier classified the collected data of up to five different activities with average accuracy of over 90%.