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

Christopher Buckley, Lisa Alcock, Ríona McArdle, Rana Zia Ur Rehman, Silvia Del Din, Claudia Mazzà, Alison J. Yarnall, Lynn Rochester

Research output: Contribution to journalArticlepeer-review

110 Citations (Scopus)
45 Downloads (Pure)

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.
Original languageEnglish
Pages (from-to)34-55
Number of pages21
JournalBrain Sciences
Volume9
Issue number2
DOIs
Publication statusPublished - 6 Feb 2019
Externally publishedYes

Keywords

  • Ataxia
  • Deep learning
  • Dementia
  • Disease phenotyping
  • Machine learning
  • Movement science
  • Parkinson’s disease
  • Risk prediction

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