Evaluation of Wellness Detection Techniques using Complex Activities Association for Smart Home Ambient

Abstract

Wireless Sensor Network based smart homes have the potential to meet the growing challenges of independent living of elderly people in smart homes. However, wellness detection of elderly people in smart homes is still a challenging research domain. Many researchers have proposed several techniques; however, majority of these techniques does not provide a comprehensive solution because complex activities cannot be determined easily and comprehensive wellness is difficult to diagnose. In this study's critical review, it has been observed that strong association lies among the vital wellness determination parameters. In this paper, an association rules based model is proposed for the simple and complex (overlapped) activities recognition and comprehensive wellness detection mechanism after analyzing existing techniques. It considers vital wellness detection parameters (temporal association of sub activity location and sub activity, time gaps between two adjacent activities, temporal association of inter and intra activities). Activity recognition and wellness detection will be performed on the basis of extracted temporal association rules and expert knowledgebase. Learning component is an important module of our proposed model to accommodate the changing trends in the frequent pattern behavior of an elderly person and recommend a caregiver/expert to adjust the expert knowledgebase according to the found abnormalities.