A Modelling of Context-Aware Elderly Healthcare Eco-System-(CA-EHS) Using Signal Analysis and Machine Learning Approach

Abstract

Advances in digital and communication technologies have led to the rapid development of advanced technologies such as machine learning, the Internet of Things, and cloud systems that automate the work process in healthcare, education, and multiple industries. Nowadays, the elderly are often left alone at home, so it is necessary to track their health and behavioral activity. In this paper, modeling a context-aware automated elderly activity monitoring system is proposed to monitor elderly biological conditions and behavioral activity changes uninterruptedly. In the proposed system, sensory data is collected through a variety of sensor devices. Based on captured sensory signals, elderly health forecasting is performed using signal processing, machine learning approaches of support vector machine, and cloud-assisted context-aware on-demand and pro-active healthcare support. The simulation outcome exhibits better performance achieved by the proposed system for elderly activity recognition and healthcare than the existing Random forest classifier. The comparative analysis shows that SVM's proposed system achieves a 94% accuracy rate in activity recognition.