Gait Analysis with Wearables Can Accurately Classify Fallers from Non-Fallers: A Step toward Better Management of Neurological Disorders

Rana Zia Ur Rehman*, Yuhan Zhou, Silvia Del Din, Lisa Alcock, Clint Hansen, Yu Guan, Tibor Hortobágyi, Walter Maetzler, Lynn Rochester, Claudine J C Lamoth

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

20 Citations (Scopus)
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Falls are the leading cause of mortality, morbidity and poor quality of life in older adults with or without neurological conditions. Applying machine learning (ML) models to gait analysis outcomes offers the opportunity to identify individuals at risk of future falls. The aim of this study was to determine the effect of different data pre-processing methods on the performance of ML models to classify neurological patients who have fallen from those who have not for future fall risk assessment. Gait was assessed using wearables in clinic while walking 20 m at a self-selected comfortable pace in 349 (159 fallers, 190 non-fallers) neurological patients. Six different ML models were trained on data pre-processed with three techniques such as standardisation, principal component analysis (PCA) and path signature method. Fallers walked more slowly, with shorter strides and longer stride duration compared to non-fallers. Overall, model accuracy ranged between 48% and 98% with 43-99% sensitivity and 48-98% specificity. A random forest (RF) classifier trained on data pre-processed with the path signature method gave optimal classification accuracy of 98% with 99% sensitivity and 98% specificity. Data pre-processing directly influences the accuracy of ML models for the accurate classification of fallers. Using gait analysis with trained ML models can act as a tool for the proactive assessment of fall risk and support clinical decision-making.

Original languageEnglish
Article number6992
Pages (from-to)1-18
Number of pages18
JournalMDPI: Sensor
Issue number23
Publication statusPublished - 1-Dec-2020


  • neurological disorders
  • machine learning
  • classification
  • fall
  • path signature
  • gait
  • inertial measurement unit
  • data pre-processing
  • fall risk assessment
  • wearables

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