  With the advancement of the Internet of Things(IoT) and pervasive computing
applications, it provides a better opportunity to understand the behavior of
the aging population. However, in a nursing home scenario, common sensors and
techniques used to track an elderly living alone are not suitable. In this
paper, we design a location-based tracking system for a four-story nursing home
- The Salvation Army, Peacehaven Nursing Home in Singapore. The main challenge
here is to identify the group activity among the nursing home's residents and
to detect if they have any deviated activity behavior. We propose a
location-based deviated activity behavior detection system to detect deviated
activity behavior by leveraging data fusion technique. In order to compute the
features for data fusion, an adaptive method is applied for extracting the
group and individual activity time and generate daily hybrid norm for each of
the residents. Next, deviated activity behavior detection is executed by
considering the difference between daily norm patterns and daily input data for
each resident. Lastly, the deviated activity behavior among the residents are
classified using a rule-based classification approach. Through the
implementation, there are 44.4% of the residents do not have deviated activity
behavior , while 37% residents involved in one deviated activity behavior and
18.6% residents have two or more deviated activity behaviors.
