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.
https://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
https://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.
https://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
https://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.
https://eprints.bournemouth.ac.uk/33552/
Source: BURO EPrints