Machine learning based identification of elderly persons with cognitive impairment using dynamic time warping

Cognitive changes in general occur with normal aging. This may lead to the prevalence and effect of age associated diseases. The understanding and identification of these age-related cognitive impairments is an important aspect in elderly population. This leads in the simple case, supporting a funct...

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Autores principales:	Kondragunta Jyothsna, Seidel Roman, Hirtz Gangolf
Formato:	article
Lenguaje:	EN
Publicado:	De Gruyter 2020
Materias:	machine learning dynamic time warping cognitive impairments classification elderly persons Medicine R
Acceso en línea:	https://doaj.org/article/aeb5e0d709964bf6a19e66c0a57209a1
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id	oai:doaj.org-article:aeb5e0d709964bf6a19e66c0a57209a1
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spelling	oai:doaj.org-article:aeb5e0d709964bf6a19e66c0a57209a12021-12-05T14:10:42ZMachine learning based identification of elderly persons with cognitive impairment using dynamic time warping2364-550410.1515/cdbme-2020-3093https://doaj.org/article/aeb5e0d709964bf6a19e66c0a57209a12020-09-01T00:00:00Zhttps://doi.org/10.1515/cdbme-2020-3093https://doaj.org/toc/2364-5504Cognitive changes in general occur with normal aging. This may lead to the prevalence and effect of age associated diseases. The understanding and identification of these age-related cognitive impairments is an important aspect in elderly population. This leads in the simple case, supporting a functional independence of the elderly and in a complex case, an early identification of dementia in advance. One important change with normal aging is the decline in gait functionality. The decline in gait is more visible in the elderly with more cognitive impairment during dual cognitive tasks, multi-tasking exercises. For the classification of the healthy elderly from the elderly having cognitive impairments, the gait data of the elderly is acquired through Kinect V2. A waking trial of 5m long is used to collect the gait data. 3D based pose estimation using the depth data is performed. Gait parameters and gait cycles of the individual elderly are estimated. In this paper, Dynamic Time Warping (DTW) algorithm is used to compare the patterns of the gait cycles of the individual in different trails such as Regular Gait 1 (RG1), Regular Gait 2 (RG2), Counting Backward 1 (CB1), Counting Backward 3 (CB3), Fast Gait (FG) and Words with Special Letters (WSPL). The identified cross levels along with the estimated gait parameters are used for training the machine learning algorithm. Support Vector Machines (SVM) were used for the classification of the elderly persons with or without cognitive impairments. The experiment results proved that such a classification of cognitive impairment levels using 3D pose estimation and machine learning helps in future for the identification of dementia in advance.Kondragunta JyothsnaSeidel RomanHirtz GangolfDe Gruyterarticlemachine learningdynamic time warpingcognitive impairmentsclassificationelderly personsMedicineRENCurrent Directions in Biomedical Engineering, Vol 6, Iss 3, Pp 360-363 (2020)
institution	DOAJ
collection	DOAJ
language	EN
topic	machine learning dynamic time warping cognitive impairments classification elderly persons Medicine R
spellingShingle	machine learning dynamic time warping cognitive impairments classification elderly persons Medicine R Kondragunta Jyothsna Seidel Roman Hirtz Gangolf Machine learning based identification of elderly persons with cognitive impairment using dynamic time warping
description	Cognitive changes in general occur with normal aging. This may lead to the prevalence and effect of age associated diseases. The understanding and identification of these age-related cognitive impairments is an important aspect in elderly population. This leads in the simple case, supporting a functional independence of the elderly and in a complex case, an early identification of dementia in advance. One important change with normal aging is the decline in gait functionality. The decline in gait is more visible in the elderly with more cognitive impairment during dual cognitive tasks, multi-tasking exercises. For the classification of the healthy elderly from the elderly having cognitive impairments, the gait data of the elderly is acquired through Kinect V2. A waking trial of 5m long is used to collect the gait data. 3D based pose estimation using the depth data is performed. Gait parameters and gait cycles of the individual elderly are estimated. In this paper, Dynamic Time Warping (DTW) algorithm is used to compare the patterns of the gait cycles of the individual in different trails such as Regular Gait 1 (RG1), Regular Gait 2 (RG2), Counting Backward 1 (CB1), Counting Backward 3 (CB3), Fast Gait (FG) and Words with Special Letters (WSPL). The identified cross levels along with the estimated gait parameters are used for training the machine learning algorithm. Support Vector Machines (SVM) were used for the classification of the elderly persons with or without cognitive impairments. The experiment results proved that such a classification of cognitive impairment levels using 3D pose estimation and machine learning helps in future for the identification of dementia in advance.
format	article
author	Kondragunta Jyothsna Seidel Roman Hirtz Gangolf
author_facet	Kondragunta Jyothsna Seidel Roman Hirtz Gangolf
author_sort	Kondragunta Jyothsna
title	Machine learning based identification of elderly persons with cognitive impairment using dynamic time warping
title_short	Machine learning based identification of elderly persons with cognitive impairment using dynamic time warping
title_full	Machine learning based identification of elderly persons with cognitive impairment using dynamic time warping
title_fullStr	Machine learning based identification of elderly persons with cognitive impairment using dynamic time warping
title_full_unstemmed	Machine learning based identification of elderly persons with cognitive impairment using dynamic time warping
title_sort	machine learning based identification of elderly persons with cognitive impairment using dynamic time warping
publisher	De Gruyter
publishDate	2020
url	https://doaj.org/article/aeb5e0d709964bf6a19e66c0a57209a1
work_keys_str_mv	AT kondraguntajyothsna machinelearningbasedidentificationofelderlypersonswithcognitiveimpairmentusingdynamictimewarping AT seidelroman machinelearningbasedidentificationofelderlypersonswithcognitiveimpairmentusingdynamictimewarping AT hirtzgangolf machinelearningbasedidentificationofelderlypersonswithcognitiveimpairmentusingdynamictimewarping
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Machine learning based identification of elderly persons with cognitive impairment using dynamic time warping

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