Measuring Gait Variables Using Computer Vision to Assess Mobility and Fall Risk in Older Adults With Dementia
Fall risk is high for older adults with dementia. Gait impairment contributes to increased fall risk, and gait changes are common in people with dementia, although the reliable assessment of gait is challenging in this population. This study aimed to develop an automated approach to performing gait...
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doaj-7cbe4b8f62bc497795b0175181493eda2021-03-29T18:41:09ZengIEEEIEEE Journal of Translational Engineering in Health and Medicine2168-23722020-01-0181910.1109/JTEHM.2020.29983269103018Measuring Gait Variables Using Computer Vision to Assess Mobility and Fall Risk in Older Adults With DementiaKimberley-Dale Ng0Sina Mehdizadeh1Andrea Iaboni2Avril Mansfield3Alastair Flint4Babak Taati5Toronto Rehabilitation Institute (KITE), University Health Network, Toronto, ON, CanadaToronto Rehabilitation Institute (KITE), University Health Network, Toronto, ON, CanadaToronto Rehabilitation Institute (KITE), University Health Network, Toronto, ON, CanadaToronto Rehabilitation Institute (KITE), University Health Network, Toronto, ON, CanadaDepartment of Psychiatry, University of Toronto, Toronto, ON, CanadaToronto Rehabilitation Institute (KITE), University Health Network, Toronto, ON, CanadaFall risk is high for older adults with dementia. Gait impairment contributes to increased fall risk, and gait changes are common in people with dementia, although the reliable assessment of gait is challenging in this population. This study aimed to develop an automated approach to performing gait assessments based on gait data that is collected frequently and unobtrusively, and analysed using computer vision methods. Recent developments in computer vision have led to the availability of open source human pose estimation algorithms, which automatically estimate the joint locations of a person in an image. In this study, a pre-existing pose estimation model was applied to 1066 walking videos collected of 31 older adults with dementia as they walked naturally in a corridor on a specialized dementia unit over a two week period. Using the tracked pose information, gait features were extracted from video recordings of gait bouts and their association with clinical mobility assessment scores and future falls data was examined. A significant association was found between extracted gait features and a clinical mobility assessment and the number of future falls, providing concurrent and predictive validation of this approach.https://ieeexplore.ieee.org/document/9103018/Computer visiondementiagaitstabilityfallspose tracking |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Kimberley-Dale Ng Sina Mehdizadeh Andrea Iaboni Avril Mansfield Alastair Flint Babak Taati |
spellingShingle |
Kimberley-Dale Ng Sina Mehdizadeh Andrea Iaboni Avril Mansfield Alastair Flint Babak Taati Measuring Gait Variables Using Computer Vision to Assess Mobility and Fall Risk in Older Adults With Dementia IEEE Journal of Translational Engineering in Health and Medicine Computer vision dementia gait stability falls pose tracking |
author_facet |
Kimberley-Dale Ng Sina Mehdizadeh Andrea Iaboni Avril Mansfield Alastair Flint Babak Taati |
author_sort |
Kimberley-Dale Ng |
title |
Measuring Gait Variables Using Computer Vision to Assess Mobility and Fall Risk in Older Adults With Dementia |
title_short |
Measuring Gait Variables Using Computer Vision to Assess Mobility and Fall Risk in Older Adults With Dementia |
title_full |
Measuring Gait Variables Using Computer Vision to Assess Mobility and Fall Risk in Older Adults With Dementia |
title_fullStr |
Measuring Gait Variables Using Computer Vision to Assess Mobility and Fall Risk in Older Adults With Dementia |
title_full_unstemmed |
Measuring Gait Variables Using Computer Vision to Assess Mobility and Fall Risk in Older Adults With Dementia |
title_sort |
measuring gait variables using computer vision to assess mobility and fall risk in older adults with dementia |
publisher |
IEEE |
series |
IEEE Journal of Translational Engineering in Health and Medicine |
issn |
2168-2372 |
publishDate |
2020-01-01 |
description |
Fall risk is high for older adults with dementia. Gait impairment contributes to increased fall risk, and gait changes are common in people with dementia, although the reliable assessment of gait is challenging in this population. This study aimed to develop an automated approach to performing gait assessments based on gait data that is collected frequently and unobtrusively, and analysed using computer vision methods. Recent developments in computer vision have led to the availability of open source human pose estimation algorithms, which automatically estimate the joint locations of a person in an image. In this study, a pre-existing pose estimation model was applied to 1066 walking videos collected of 31 older adults with dementia as they walked naturally in a corridor on a specialized dementia unit over a two week period. Using the tracked pose information, gait features were extracted from video recordings of gait bouts and their association with clinical mobility assessment scores and future falls data was examined. A significant association was found between extracted gait features and a clinical mobility assessment and the number of future falls, providing concurrent and predictive validation of this approach. |
topic |
Computer vision dementia gait stability falls pose tracking |
url |
https://ieeexplore.ieee.org/document/9103018/ |
work_keys_str_mv |
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