Comparison Analysis of Radio_Based and Sensor_Based Wearable Human Activity Recognition Systems

Abstract

Human activity recognition (HAR) systems aim to provide low-cost, low-power, unobtrusive and non-invasive solutions to monitor and collect data accurately for human-centric applications, such as health monitoring, assisted living and rehabilitation. Although wearable sensor_based HAR systems have been demonstrated to be effective in the literature, they raise various concerns such as energy consumption and hardware cost. In this work, we examine the pattern of radio signal strength variations in different activity classes in absence of sensor hardware. We present a performance comparison analysis by setting up two testbeds to compare a sensor_based with a radio_based HAR system over a range of variable metrics such as the number of sensor nodes, and the nodes and the sink node placement with respect to the accuracy and the energy efficiency. Wearable HAR datasets are constructed based on our reported testbeds. The main contributions of this work are in two folds: (1) when eliminating the use of accelerometers in the radio_based system, beside the reduced hardware cost, prolonged lifetime of the HAR system by nearly 30% can be achieved while maintaining the accuracy. The impact of the selected overlapping window size (WS) is also investigated with respect to the accuracy level in both systems over a range of activity classes. (2) The impact of the node placement on the accuracy indicates a higher dependency to the number of nodes, the nodes and the sink node placements in the radio_based system due to the dependency of the results to the distance.