Detection of abnormal behaviour for dementia sufferers using Convolutional Neural Networks.
In recent years, there is a rapid increase in the population of elderly people. However, elderly people may suffer from the consequences of cognitive decline, which is a mental health disorder that primarily affects cognitive abilities such as learning, memory, etc. As a result, the elderly people m...
| Publicado en: | Artificial Intelligence in Medicine Vol. 94; pp. 88 - 96 |
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| Autores principales: | , |
| Formato: | Journal Article |
| Publicado: |
Elsevier B.V.
Mar2019
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=135227268&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 135227268 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 09333657 3HY jtl: Artificial Intelligence in Medicine issn: 09333657 maglogo: N pubinfo: dt: Mar2019 vid: 94 pid: 1004 pub: Elsevier B.V. artinfo: ui: 135227268 135227268 NLM30871686 10.1016/j.artmed.2019.01.005 NLM30871686 135227268 ppf: 88 ppct: 8 formats: tig: atl: Detection of abnormal behaviour for dementia sufferers using Convolutional Neural Networks. aug: au: Arifoglu, Damla Bouchachia, Abdelhamid affil: Department of Computing and Informatics, Faculty of Science and Technology, Bournemouth University, UK sug: ab: In recent years, there is a rapid increase in the population of elderly people. However, elderly people may suffer from the consequences of cognitive decline, which is a mental health disorder that primarily affects cognitive abilities such as learning, memory, etc. As a result, the elderly people may get dependent on caregivers to complete daily life tasks. Detecting the early indicators of dementia before it gets worsen and warning the caregivers and medical doctors would be helpful for further diagnosis. In this paper, the problem of activity recognition and abnormal behaviour detection is investigated for elderly people with dementia. First of all, the paper presents a methodology for generating synthetic data reflecting on some behavioural difficulties of people with dementia given the difficulty of obtaining real-world data. Secondly, the paper explores Convolutional Neural Networks (CNNs) to model patterns in activity sequences and detect abnormal behaviour related to dementia. Activity recognition is considered as a sequence labelling problem, while abnormal behaviour is flagged based on the deviation from normal patterns. Moreover, the performance of CNNs is compared against the state-of-art methods such as Naïve Bayes (NB), Hidden Markov Models (HMMs), Hidden Semi-Markov Models (HSMM), Conditional Random Fields (CRFs). The results obtained indicate that CNNs are competitive with those state-of-art methods. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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