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Article

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

1
Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, Newcastle Upon Tyne NE4 5PL, UK
2
Department of Human Movement Sciences, University Medical Center Groningen, University of Groningen, 9713 AV Groningen, The Netherlands
3
Department of Neurology, University Hospital Schleswig-Holstein, Campus Kiel, 24105 Kiel, Germany
4
School of Computing, Newcastle University, Newcastle Upon Tyne NE4 5TG, UK
5
Newcastle upon Tyne Hospitals NHS Foundation Trust, Newcastle Upon Tyne NE7 7DN, UK
*
Author to whom correspondence should be addressed.
These authors contributed equally to the research.
Sensors 2020, 20(23), 6992; https://doi.org/10.3390/s20236992
Received: 24 October 2020 / Revised: 28 November 2020 / Accepted: 4 December 2020 / Published: 7 December 2020
(This article belongs to the Special Issue Wearable Sensors for Movement Analysis)
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. View Full-Text
Keywords: neurological disorders; machine learning; classification; fall; path signature; gait; inertial measurement unit; data pre-processing; fall risk assessment; wearables neurological disorders; machine learning; classification; fall; path signature; gait; inertial measurement unit; data pre-processing; fall risk assessment; wearables
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MDPI and ACS Style

Rehman, R.Z.U.; Zhou, Y.; Del Din, S.; Alcock, L.; Hansen, C.; Guan, Y.; Hortobágyi, T.; Maetzler, W.; Rochester, L.; Lamoth, C.J.C. Gait Analysis with Wearables Can Accurately Classify Fallers from Non-Fallers: A Step toward Better Management of Neurological Disorders. Sensors 2020, 20, 6992. https://doi.org/10.3390/s20236992

AMA Style

Rehman RZU, Zhou Y, Del Din S, Alcock L, Hansen C, Guan Y, Hortobágyi T, Maetzler W, Rochester L, Lamoth CJC. Gait Analysis with Wearables Can Accurately Classify Fallers from Non-Fallers: A Step toward Better Management of Neurological Disorders. Sensors. 2020; 20(23):6992. https://doi.org/10.3390/s20236992

Chicago/Turabian Style

Rehman, Rana Z.U., Yuhan Zhou, Silvia Del Din, Lisa Alcock, Clint Hansen, Yu Guan, Tibor Hortobágyi, Walter Maetzler, Lynn Rochester, and Claudine J.C. Lamoth. 2020. "Gait Analysis with Wearables Can Accurately Classify Fallers from Non-Fallers: A Step toward Better Management of Neurological Disorders" Sensors 20, no. 23: 6992. https://doi.org/10.3390/s20236992

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