Using Learning Techniques to Observe
Elderly’s Behavior Changes over Time
in Smart Home
Dorsaf Zekri
1,2(B) , Thierry Delot
1(B) , Mikael Desertot
1(B) ,
Sylvain Lecomte
1(B) , and Marie Thilliez
1(B)
1 Universit´ e Polytechnique Hauts-de-France, LAMIH UMR CNRS 8201,
Hauts-de-France, France
{Dorsaf.Zekri2,Thierry.Delot,Mikael.Desertot,Sylvain.Lecomte,
Marie.Thilliez}@uphf.fr
2 ReDCAD Laboratory, University of Sfax, B.P. 1173 Sfax, Tunisia
Abstract. Smart environments and technology used for elder care,
increases independent living time and cuts long-term care costs. A key
requirement for these systems consists in detecting and informing about
abnormal behavior in users’routines. In this paper, our objective is to
automatically observe the elderly behavior over time and detect anomalies that may occur on the long term. Therefore, we propose a learning
method to formalize a normal behavior pattern for each elderly people
related to his Activities of Daily Living (ADL). We also adopt a temporal similarity score between activities that allows to detect behavior
changes over time. In change behavior period we focus on each activity
to detect anomalies. A use case with real datasets are promising.
Keywords: Behavior change observation · Elderly people · Smart
home · Activities of Daily Living
1 Introduction
With the growing elderly population, research in elderly living and well-being
has been aimed toward medical analysis and supporting independent living of
elderly people. Elderly people are often disabled by several interacting problems,
such as loss of function and social and environmental factors. All these factors,
separately or together, 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 of elderly people
with medical issues, such as diabetes and cognitive disabilities, by analyzing the
behavior of residents within sensor-based environments. The progress of technology (wearable sensors, smart phones and other mobile devices, wireless communications, etc.) enables the development of effective solutions to help older
people to live independently in their homes.
c
The Author(s) 2020
M. Jmaiel et al. (Eds.): ICOST 2020, LNCS 12157, pp. 129–141, 2020.
https://doi.org/10.1007/978-3-030-51517-1_11
Elderly’s Behavior Changes over Time
in Smart Home
Dorsaf Zekri
1,2(B) , Thierry Delot
1(B) , Mikael Desertot
1(B) ,
Sylvain Lecomte
1(B) , and Marie Thilliez
1(B)
1 Universit´ e Polytechnique Hauts-de-France, LAMIH UMR CNRS 8201,
Hauts-de-France, France
{Dorsaf.Zekri2,Thierry.Delot,Mikael.Desertot,Sylvain.Lecomte,
Marie.Thilliez}@uphf.fr
2 ReDCAD Laboratory, University of Sfax, B.P. 1173 Sfax, Tunisia
Abstract. Smart environments and technology used for elder care,
increases independent living time and cuts long-term care costs. A key
requirement for these systems consists in detecting and informing about
abnormal behavior in users’routines. In this paper, our objective is to
automatically observe the elderly behavior over time and detect anomalies that may occur on the long term. Therefore, we propose a learning
method to formalize a normal behavior pattern for each elderly people
related to his Activities of Daily Living (ADL). We also adopt a temporal similarity score between activities that allows to detect behavior
changes over time. In change behavior period we focus on each activity
to detect anomalies. A use case with real datasets are promising.
Keywords: Behavior change observation · Elderly people · Smart
home · Activities of Daily Living
1 Introduction
With the growing elderly population, research in elderly living and well-being
has been aimed toward medical analysis and supporting independent living of
elderly people. Elderly people are often disabled by several interacting problems,
such as loss of function and social and environmental factors. All these factors,
separately or together, 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 of elderly people
with medical issues, such as diabetes and cognitive disabilities, by analyzing the
behavior of residents within sensor-based environments. The progress of technology (wearable sensors, smart phones and other mobile devices, wireless communications, etc.) enables the development of effective solutions to help older
people to live independently in their homes.
c
The Author(s) 2020
M. Jmaiel et al. (Eds.): ICOST 2020, LNCS 12157, pp. 129–141, 2020.
https://doi.org/10.1007/978-3-030-51517-1_11