Activity recognition using machine learning techniques for smart home assisted living

Abstract

The statistical survey by United Nations Department of Economic and Social Affairs/Population Division says, quotglobally the number of persons aged 60 and above is expected to be more than double by 2050 newlineand more than triple by 2100quot. Especially in India, 9.5 percent of the population comprises of elders above 60 years. This may reach 22.2 percent in 2050 and 44.4 percent in 2100. On one side, the population of newlineelders are gradually increasing and on the other side there is a challenge to take care of the wellbeing of the elders when they are living alone. Smart home assisted living system can address these problems. Smart newlineHome Assisted living System is one among the growing research areas in smart computing. Advances in sensing, communication and ambientintelligence technologies created tremendous change in smart living newlineenvironment. The development in technology made smart home to support elders, disabled persons and the needy person. newlineActivity recognition is a growing technology in recent research and it plays a vital role in smart home assisted living system. Activity Recognition is a more dynamic, interesting, and challenging research newlinetopic in different areas like Ubiquitous Computing, Smart Home Assisted Living, Human Computer Interaction (HIC) etc. It provides solution to various real-time, human-oriented problems like elder care and health newlinecare. newlineIn order to address the issue on providing support on elder care this research proposes a machine learning based activity recognition model and an enhanced communication protocol for a smart home system, which are collaborated for designing the architecture of a smart home assisted living system. This system consists of three sub phases viz., data acquisition, monitoring system, and tracking system. newline