Energy-efficient recognition of human activity in body sensor networks via compressed classification

Energy efficiency is an important challenge to broad deployment of wireless body sensor networks for long-term physical movement monitoring. Inspired by theories of sparse representation and compressed sensing, the power-aware compressive classification approach SRC-DRP (sparse representation–based classification with distributed random projection) for activity recognition is proposed, which integrates data compressing and classification. Random projection as a data compression tool is individually implemented on each sensor node to reduce the amount of data for transmission. Compressive classification can be applied directly on the compressed samples received from all nodes. This method was validated on the Wearable Action Recognition Dataset and implemented on embedded nodes for offline and online experiments. It is shown that our method reduces energy consumption by approximately 20% while maintaining an activity recognition accuracy of 88% at a compression ratio of 0.5.