Detecting indicators of cognitive impairment via Graph Convolutional Networks

This source preferred by Hamid Bouchachia

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

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

Journal: Engineering Applications of Artificial Intelligence

Volume: 89

ISSN: 0952-1976

DOI: 10.1016/j.engappai.2019.103401

© 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.

This data was imported from DBLP:

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

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

Journal: Eng. Appl. Artif. Intell.

Volume: 89

Pages: 103401

This data was imported from Scopus:

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

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

Journal: Engineering Applications of Artificial Intelligence

Volume: 89

ISSN: 0952-1976

DOI: 10.1016/j.engappai.2019.103401

© 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.

This data was imported from Web of Science (Lite):

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

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

Journal: ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE

Volume: 89

eISSN: 1873-6769

ISSN: 0952-1976

DOI: 10.1016/j.engappai.2019.103401

The data on this page was last updated at 05:19 on October 21, 2020.