2023
DOI: 10.3390/s23020679
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A Machine Learning Approach for Walking Classification in Elderly People with Gait Disorders

Abstract: Walking ability of elderly individuals, who suffer from walking difficulties, is limited, which restricts their mobility independence. The physical health and well-being of the elderly population are affected by their level of physical activity. Therefore, monitoring daily activities can help improve the quality of life. This becomes especially a huge challenge for those, who suffer from dementia and Alzheimer’s disease. Thus, it is of great importance for personnel in care homes/rehabilitation centers to moni… Show more

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Cited by 8 publications
(7 citation statements)
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References 75 publications
(84 reference statements)
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“…Patients in one Danish municipality who have trouble walking were monitored with accelerometers, and an (ML) based system was developed by Peimankar et al [23]. The ML algorithm can reliably categorise the walking behaviour of people with a variety of walking disorders.…”
Section: Related Workmentioning
confidence: 99%
“…Patients in one Danish municipality who have trouble walking were monitored with accelerometers, and an (ML) based system was developed by Peimankar et al [23]. The ML algorithm can reliably categorise the walking behaviour of people with a variety of walking disorders.…”
Section: Related Workmentioning
confidence: 99%
“…Utilizing assistive devices such as crutches, canes, and walkers provides a practical approach to acquiring training datasets for machine learning algorithms in osteoporosis management [28]. These devices, designed to support and restore normal locomotion in individuals affected by injuries or pathologies, are easily equipped with sensors to capture gait and posture data [29,30]. A smart walker, for instance, functions as a minimalist tool in gait classification system development, simultaneously supporting users while acquiring kinetic data [30].…”
Section: Introductionmentioning
confidence: 99%
“…These devices, designed to support and restore normal locomotion in individuals affected by injuries or pathologies, are easily equipped with sensors to capture gait and posture data [29,30]. A smart walker, for instance, functions as a minimalist tool in gait classification system development, simultaneously supporting users while acquiring kinetic data [30]. Moreover, while video-based gait analysis raises privacy issues, smart walkers collect data solely from the user's interaction with the device, thus alleviating privacy concerns.…”
Section: Introductionmentioning
confidence: 99%
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“…ML has emerged as a promising approach for clinical decision support tools across various health care domains. ML techniques have been successfully employed in diverse areas, including the detection of dementia and Alzheimer’s disease [ 20 , 21 ], early detection of diabetes [ 22 ], detection of atrial fibrillation [ 23 ], as well as the early detection of AUD, among others.…”
Section: Introductionmentioning
confidence: 99%