Detecting indicators of cognitive impairment via Graph Convolutional Networks

Authors: Arifoglu, D., Charif, H.N. and Bouchachia, A.

Journal: Engineering Applications of Artificial Intelligence

Volume: 89

ISSN: 0952-1976

DOI: 10.1016/j.engappai.2019.103401

Abstract:

While the life expectancy is on the rise all over the world, more people face health related problems such as cognitive decline. Dementia is a name used to describe progressive brain syndromes affecting memory, thinking, behaviour and emotion. People suffering from dementia may lose their abilities to perform daily life activities and they become on their caregivers. Hence, detecting the indicators of cognitive decline and warning the caregivers and medical doctors for further diagnosis would be helpful. In this study, we tackle the problem of activity recognition and abnormal behaviour detection in the context of dementia by observing daily life patterns of elderly people. Since there is no real-world data available, firstly a method is presented to simulate abnormal behaviour that can be observed in daily activity patterns of dementia sufferers. Secondly, Graph Convolutional Networks (GCNs) are exploited to recognise activities based on their granular-level sensor activations. Thirdly, abnormal behaviour related to dementia is detected using activity recognition confidence probabilities. Lastly, GCNs are compared against the state-of-the-art methods. The results obtained indicate that GCNs are able to recognise activities and flag abnormal behaviour related to dementia.

http://eprints.bournemouth.ac.uk/33552/

Source: Scopus

Detecting indicators of cognitive impairment via Graph Convolutional Networks

Authors: Arifoglu, D., Charif, H.N. and Bouchachia, A.

Journal: ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE

Volume: 89

eISSN: 1873-6769

ISSN: 0952-1976

DOI: 10.1016/j.engappai.2019.103401

http://eprints.bournemouth.ac.uk/33552/

Source: Web of Science (Lite)

Detecting indicators of cognitive impairment via Graph Convolutional Networks

Authors: Arifoglu, D., Charif, H.N. and Bouchachia, A.

Journal: Engineering Applications of Artificial Intelligence

Volume: 89

ISSN: 0952-1976

DOI: 10.1016/j.engappai.2019.103401

Abstract:

© 2020 Elsevier Ltd While the life expectancy is on the rise all over the world, more people face health related problems such as cognitive decline. Dementia is a name used to describe progressive brain syndromes affecting memory, thinking, behaviour and emotion. People suffering from dementia may lose their abilities to perform daily life activities and they become on their caregivers. Hence, detecting the indicators of cognitive decline and warning the caregivers and medical doctors for further diagnosis would be helpful. In this study, we tackle the problem of activity recognition and abnormal behaviour detection in the context of dementia by observing daily life patterns of elderly people. Since there is no real-world data available, firstly a method is presented to simulate abnormal behaviour that can be observed in daily activity patterns of dementia sufferers. Secondly, Graph Convolutional Networks (GCNs) are exploited to recognise activities based on their granular-level sensor activations. Thirdly, abnormal behaviour related to dementia is detected using activity recognition confidence probabilities. Lastly, GCNs are compared against the state-of-the-art methods. The results obtained indicate that GCNs are able to recognise activities and flag abnormal behaviour related to dementia.

http://eprints.bournemouth.ac.uk/33552/

Source: Manual

Preferred by: Hamid Bouchachia

Detecting indicators of cognitive impairment via Graph Convolutional Networks.

Authors: Arifoglu, D., Nait-Charif, H. and Bouchachia, A.

Journal: Eng. Appl. Artif. Intell.

Volume: 89

Pages: 103401

DOI: 10.1016/j.engappai.2019.103401

http://eprints.bournemouth.ac.uk/33552/

Source: DBLP

Detecting indicators of cognitive impairment via Graph Convolutional Networks

Authors: Arifoglu, D., Nait-Charif, H. and Bouchachia, A.

Journal: Engineering Applications of Artificial Intelligence

Volume: 89

Issue: March

ISSN: 0952-1976

Abstract:

While the life expectancy is on the rise all over the world, more people face health related problems such as cognitive decline. Dementia is a name used to describe progressive brain syndromes affecting memory, thinking, behaviour and emotion. People suffering from dementia may lose their abilities to perform daily life activities and they become on their caregivers. Hence, detecting the indicators of cognitive decline and warning the caregivers and medical doctors for further diagnosis would be helpful. In this study, we tackle the problem of activity recognition and abnormal behaviour detection in the context of dementia by observing daily life patterns of elderly people. Since there is no real-world data available, firstly a method is presented to simulate abnormal behaviour that can be observed in daily activity patterns of dementia sufferers. Secondly, Graph Convolutional Networks (GCNs) are exploited to recognise activities based on their granular-level sensor activations. Thirdly, abnormal behaviour related to dementia is detected using activity recognition confidence probabilities. Lastly, GCNs are compared against the state-of-the-art methods. The results obtained indicate that GCNs are able to recognise activities and flag abnormal behaviour related to dementia.

http://eprints.bournemouth.ac.uk/33552/

Source: BURO EPrints