Remote Monitoring and Recognition of Physical Activities of Elderly People Using Smartphone Accelerometer Sensor Data Using Deep Learning Models

Abstract

Remote monitoring and recognition of physical activities of elderly people within smart homes and detection of the deviations in their daily activities from previous behavior is one of the fundamental research challenges for the development of ambient assisted living system. This system is also very helpful in monitoring the health of a swiftly aging population in the developed countries. In this chapter, a framework is proposed for remote monitoring and recognition of physical activities of elderly people using smart phone accelerometer sensor data using deep learning models. The main objective of the proposed framework is to provide preventive measures for the emergency health issues such as cardiac arrest, sudden falls, dementia, or arthritis. For the performance evaluation of the proposed framework, two different benchmark accelerometer sensor datasets, UCI and WISDM, are used. Results analysis confirms the performance of the proposed scheme in terms of accuracy, F1-score, root-mean square error (RMSE).