Context-aware Human Activity Recognition and decision making

Abstract

Ubiquitous Life Care (u-Life care) nowadays becomes more attractive to computer science researchers due to a demand on a high quality and low cost of care services at anytime and anywhere. Many works exploit sensor networks to monitor patient's health status, movements, and real-time daily life activities to provide care services to them. Context information with real-time daily life activities can help in better services, service suggestions, and change in system behavior for better healthcare. Our proposed Secured Wireless Sensor Network - integrated Cloud Computing for ubiquitous - Life Care (SC <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> ) monitors human health as well as activities. In this paper we focus on Human Activity Recognition Engine (HARE) framework architecture, backbone of SC <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> and discussed it in detail. Camera-based and sensor-based activity recognition engines are discussed in detail along with the manipulation of recognized activities using Context-aware Activity Manipulation Engine (CAME) and Intelligent Life Style Provider (i-LiSP). Preliminary results of CAME showed robust and accurate response to medical emergencies. We have deployed five different activity recognition engines on Cloud to identify different set of activities of Alzheimer's disease patients.