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

1
Institute of Neuroscience/Institute for Ageing, Newcastle University, Newcastle Upon Tyne NE4 5PL, UK
2
Department of Mechanical Engineering, Sheffield University, Sheffield S1 3JD, UK
3
The Newcastle upon Tyne Hospitals NHS Foundation Trust, Newcastle Upon Tyne NE7 7DN, UK
*
Author to whom correspondence should be addressed.
These authors contributed equally.
Brain Sci. 2019, 9(2), 34; https://doi.org/10.3390/brainsci9020034
Received: 18 January 2019 / Accepted: 31 January 2019 / Published: 6 February 2019
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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. View Full-Text
Keywords: movement science; Parkinson’s disease; ataxia; dementia; machine learning; deep learning; risk prediction; disease phenotyping movement science; Parkinson’s disease; ataxia; dementia; machine learning; deep learning; risk prediction; disease phenotyping
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Buckley, C.; Alcock, L.; McArdle, R.; Rehman, R.Z.U.; Del Din, S.; Mazzà, C.; Yarnall, A.J.; Rochester, L. The Role of Movement Analysis in Diagnosing and Monitoring Neurodegenerative Conditions: Insights from Gait and Postural Control. Brain Sci. 2019, 9, 34.

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