Fall detection and human activity classification using wearable sensors and compressed sensing

Abstract

The fall of elderly patients is still a critical medical issue since it can cause irreversible bone injuries due to the elderly bones weakness. To mitigate the likelihood of the occurrence of a fall, continuously tracking the patients with balance and health issues has been envisaged, despite being unpractical. To address this problem, we propose an efficient automatic fall detection system which is also fitted for the detection of different activities of daily living (ADL). The system relies on a wearable Shimmer device, to transmit some inertial signals via a wireless connection to a computer. Aiming at reducing the size of the transmitted data and minimizing the energy consumption, a compressive sensing (CS) method is applied. In this perspective, we started by creating our dataset from 17 subjects performing a set of movements, then three distinct systems were investigated: one which detects the presence or the absence of the fall, a second which detects static or dynamic movements including the fall, and a third which recognizes the fall and six other ADL activities. In the acquisition and classification steps, first only the data collected by the accelerometer are exploited, then a mixture of the accelerometer and gyroscope measurements are taken into consideration. The two configurations are compared and the resulting system incorporating CS capabilities is shown to achieve up to 99.8% of accuracy.