Role of Wearable Sensors with Machine Learning Approaches in Gait Analysis for Parkinson's Disease Assessment: A Review

Aishwarya Balakrishnan, Jeevan Medikonda, Pramod Kesavan Namboothiri, Manikandan Natarajan

Research output: Contribution to journalReview articlepeer-review

1 Citation (Scopus)

Abstract

Gait analysis, a way of assessing the manner of walking, is considered a significant criterion in diagnosing movement disorder. Various factors contribute to the alterations in gait patterns, of which neurodegenerative related disorders play a major role. Subjects affected by Parkinson's disease (PD) suffer from numerous gait-related disturbances, eventually worsening the Quality of Life. Artificial intelligence-based tools have shown great interest in computer-assisted diagnosis with the recent advancements in technology. This review article aims at portraying a novel collective approach of accenting every facet of PD gait by emphasizing the role of quantitative gait analysis and state-of-art technologies in the betterment of clinical diagnosis. The paper includes all the relevant research works (2014-2021) regarding PD assessments categorized as 1) Classification of PD and Healthy subjects, 2) PD severity prediction, and 3) Freezing of Gait detection by only considering gait modality as the mode of assessment.

Original languageEnglish
Pages (from-to)5-19
Number of pages15
JournalEngineered Science
Volume19
DOIs
Publication statusPublished - 2022

All Science Journal Classification (ASJC) codes

  • Engineering(all)
  • Physical and Theoretical Chemistry
  • Chemistry (miscellaneous)
  • Materials Science(all)
  • Energy Engineering and Power Technology
  • Artificial Intelligence
  • Applied Mathematics

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