A Framework for Detecting and Analyzing Behavior Changes of Elderly People over Time Using Learning Techniques.

Dorsaf Zekri,Marie Thilliez,Sylvain Lecomte,Mikael Desertot,Thierry Delot

doi:10.3390/s20247112

Abstract

A sensor-rich environment can be exploited for elder healthcare applications. In this work, our objective was to conduct a continuous and long-term analysis of elderly’s behavior for detecting changes. We indeed did not study snapshots of the behavior but, rather, analyzed the overall behavior evolution over long periods of time in order to detect anomalies. Therefore, we proposed a learning method and formalize a normal behavior pattern for elderly people related to her/his Activities of Daily Living (ADL). We also defined a temporal similarity score between activities that allows detecting behavior changes over time. During the periods of time when behavior changes occurred, we then focused on each activity to identify anomalies. Finally, when a behavior change occurred, it was also necessary to help caregivers and/or family members understand the possible pathology detected in order for them to react accordingly. Therefore, the framework presented in this article includes a fuzzy logic-based decision support system that provides information about the suspected disease and its severity.

Highlights

With the growing elderly population [1,2], numerous research works have focused on preserving independent living of elderly people
Previous research works focus on the analysis of the behavior to detect possible anomalies in the elderly person’s behavior
We introduce our model to characterize the normal behavior pattern for elderly people, which can be used to detect behavior changes over time by comparing the current behavioral data of an elderly with her/his usual behavior pattern

Summary

Introduction

With the growing elderly population [1,2], numerous research works have focused on preserving independent living of elderly people. People often suffer from several interacting problems [3,4], due to loss of function or difficulties in interacting with their environment. All these factors, separately or together, obviously determine the elderly person’s level of independence and influence his/her quality of life. Separately or together, obviously determine the elderly person’s level of independence and influence his/her quality of life In this context, most researchers aim to improve the living conditions of elderly people with respect to medical issues, such as diabetes or cognitive disabilities, by analyzing the behavior of residents within sensor-based environments [5]. Knowledge extracted from these data can be used to enrich the Sensors 2020, 20, 7112; doi:10.3390/s20247112 www.mdpi.com/journal/sensors

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Results

Conclusion