An Adaptive Algorithm to Improve Energy Efficiency in Wearable Activity Recognition Systems

TLDR: A feedback controller algorithm to dynamically adapt sampling rate for maintaining the tradeoff between the energy efficiency and accuracy is proposed, which nearly doubles the activity recognition system lifetime.

Abstract: Activity recognition systems are used in rehabilitation centres to monitor activity of daily living in order to assess daily functional status of elderly. A low-cost, non-invasive, and continuous wearable activity monitoring system can be realized by one or multiple wearable sensor nodes to form a self-managing wireless medical body area network. There are several arising challenges essential to be dealt within developing wearable activity recognition systems, namely sensor node lifetime and detection accuracy. This paper investigates existing solutions, which address the key opposing challenges. We propose a feedback controller algorithm to dynamically adapt sampling rate for maintaining the tradeoff between the energy efficiency and accuracy. The Number of samples and transmitted data packets is the main sources of energy consumption that impacts the system accuracy. To validate the accuracy of our proposed algorithm, a public wearable activity recognition data set is constructed. The data set is collected from 20 healthy subjects over 7 activity types excluding the transition states, using up to four accelerometer sensors connected with IEEE 802.15.4 enabled nodes in our setup. Our proposed feedback controller algorithm nearly doubles the activity recognition system lifetime. This, in turn improves the users’ quality of experience by reducing the demand for battery replacements while the accuracy of detection is maintained at the same level.