The Role of Movement Analysis in Diagnosing and Monitoring Neurodegenerative Conditions: Insights from Gait and Postural Control

Buckley, Christopher, Alcock, Lisa, McArdle, Ríona, Rehman, Rana Zia Ur, Del Din, Silvia, Mazzà, Claudia, Yarnall, Alison J. and Rochester, Lynn (2019) The Role of Movement Analysis in Diagnosing and Monitoring Neurodegenerative Conditions: Insights from Gait and Postural Control. Brain Sciences, 9 (2). p. 34. ISSN 2076-3425

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Official URL: https://doi.org/10.3390/brainsci9020034

Abstract

Quantifying gait and postural control adds valuable information that aids in understanding neurological conditions where motor symptoms predominate and cause considerable functional impairment. Disease-specific clinical scales exist; however, they are often susceptible to subjectivity, and can lack sensitivity when identifying subtle gait and postural impairments in prodromal cohorts and longitudinally to document disease progression. Numerous devices are available to objectively quantify a range of measurement outcomes pertaining to gait and postural control; however, efforts are required to standardise and harmonise approaches that are specific to the neurological condition and clinical assessment. Tools are urgently needed that address a number of unmet needs in neurological practice. Namely, these include timely and accurate diagnosis; disease stratification; risk prediction; tracking disease progression; and decision making for intervention optimisation and maximising therapeutic response (such as medication selection, disease staging, and targeted support). Using some recent examples of research across a range of relevant neurological conditions—including Parkinson’s disease, ataxia, and dementia— we will illustrate evidence that supports progress against these unmet clinical needs. We summarise the novel ‘big data’ approaches that utilise data mining and machine learning techniques to improve disease classification and risk prediction, and conclude with recommendations for future direction.

Item Type: Article
Uncontrolled Keywords: Ataxia, Deep learning, Dementia, Disease phenotyping, Machine learning, Movement science, Parkinson’s disease, Risk prediction
Subjects: A900 Others in Medicine and Dentistry
B100 Anatomy, Physiology and Pathology
C600 Sports Science
Department: Faculties > Health and Life Sciences > Sport, Exercise and Rehabilitation
Depositing User: Rachel Branson
Date Deposited: 28 Jul 2020 10:34
Last Modified: 28 Jul 2020 10:45
URI: http://nrl.northumbria.ac.uk/id/eprint/43897

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