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The classification of minor gait alterations using wearable sensors and deep learning

Turner, Alexander; Hayes, Stephen


Alexander Turner

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Mr Steve Hayes
Lecturer in Biomechanics & Strength and Conditioning


Objective: This paper describes how non-invasive wearable sensors can be used in combination with deep learning to classify artificially induced gait alterations without the requirement for a medical professional or gait analyst to be present. This approach is motivated by the goal of diagnosing gait abnormalities on a symptom-by-symptom basis, irrespective of other neuromuscular movement disorders the patients may be affected by. This could lead to improvements in treatment and offer a greater insight into movement disorders. Methods: In-shoe pressure was measured for 12 able-bodied participants, each subject to eight artificially induced gait alterations, achieved by modifying the underside of the shoe. The data were recorded at 100 Hz over 2520 data channels and were analyzed using the deep learning architecture and the long term short term memory networks. Additionally, the rationale for the decision-making process of these networks was investigated. Conclusion: Long term short term memory networks are applicable to the classification of the gait function. The classifications can be made using only 2 s of sparse data (82.0% accuracy over 96 000 instances of test data) from participants who were not a part of the training set. Significance: This paper provides potential for the gait function to be accurately classified using non-invasive techniques, and at more regular intervals, outside of a clinical setting, without the need for healthcare professionals to be present.


Turner, A., & Hayes, S. (2019). The classification of minor gait alterations using wearable sensors and deep learning. IEEE transactions on bio-medical engineering / Bio-medical Engineering Group, 66(11), 3136-3145.

Journal Article Type Article
Acceptance Date Feb 17, 2019
Online Publication Date Feb 21, 2019
Publication Date Oct 18, 2019
Deposit Date May 28, 2019
Publicly Available Date Oct 27, 2022
Journal IEEE Transactions on Biomedical Engineering
Print ISSN 0018-9294
Electronic ISSN 1558-2531
Publisher Institute of Electrical and Electronics Engineers
Peer Reviewed Peer Reviewed
Volume 66
Issue 11
Pages 3136-3145
Keywords Deep learning; Footwear; Sensors; Foot; Legged locomotion; Parkinson's Disease; Feature extraction
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Article (975 Kb)

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