Location-based Activity Behavior Deviation Detection for Nursing Home using IoT Devices

TL;DR

This paper presents a location-based IoT system for nursing homes to detect deviations in residents' activity behaviors, using data fusion and rule-based classification to improve elderly care monitoring.

Contribution

It introduces a novel location-based tracking and deviation detection system tailored for nursing homes, employing data fusion and adaptive feature extraction methods.

Findings

44.4% residents show no deviation in activity behavior

37% residents exhibit one deviated activity behavior

18.6% residents have multiple deviated behaviors

Abstract

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.