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Article

Flexible Machine Learning Algorithms for Clinical Gait Assessment Tools

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Department of Rehabilitation Medicine, University Medical Center Groningen, University of Groningen, 9713 GZ Groningen, The Netherlands
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Department of Human Movement Sciences, University Medical Center Groningen, University of Groningen, 9713 GZ Groningen, The Netherlands
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Oro Muscles B.V., 9715 CJ Groningen, The Netherlands
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Department of Medical Biochemistry and Microbiology, Uppsala University, 751 23 Uppsala, Sweden
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Department of Biomedical Engineering, University Medical Center Groningen, University of Groningen, 9713 GZ Groningen, The Netherlands
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Center for Development and Innovation (CDI), University Medical Center Groningen, University of Groningen, 9713 GZ Groningen, The Netherlands
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Data Science Center in Health (DASH), University Medical Center Groningen, University of Groningen, 9713 GZ Groningen, The Netherlands
*
Author to whom correspondence should be addressed.
Academic Editor: Mario Munoz-Organero
Sensors 2022, 22(13), 4957; https://doi.org/10.3390/s22134957
Received: 19 May 2022 / Revised: 16 June 2022 / Accepted: 27 June 2022 / Published: 30 June 2022
(This article belongs to the Special Issue Sensor Technologies for Gait Analysis)
The current gold standard of gait diagnostics is dependent on large, expensive motion-capture laboratories and highly trained clinical and technical staff. Wearable sensor systems combined with machine learning may help to improve the accessibility of objective gait assessments in a broad clinical context. However, current algorithms lack flexibility and require large training datasets with tedious manual labelling of data. The current study tests the validity of a novel machine learning algorithm for automated gait partitioning of laboratory-based and sensor-based gait data. The developed artificial intelligence tool was used in patients with a central neurological lesion and severe gait impairments. To build the novel algorithm, 2% and 3% of the entire dataset (567 and 368 steps in total, respectively) were required for assessments with laboratory equipment and inertial measurement units. The mean errors of machine learning-based gait partitions were 0.021 s for the laboratory-based datasets and 0.034 s for the sensor-based datasets. Combining reinforcement learning with a deep neural network allows significant reduction in the size of the training datasets to <5%. The low number of required training data provides end-users with a high degree of flexibility. Non-experts can easily adjust the developed algorithm and modify the training library depending on the measurement system and clinical population. View Full-Text
Keywords: clinical gait analysis; gait partitioning; machine learning; wearables; inertial measurement units; sensors; deep neural networks; reinforcement learning clinical gait analysis; gait partitioning; machine learning; wearables; inertial measurement units; sensors; deep neural networks; reinforcement learning
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MDPI and ACS Style

Greve, C.; Tam, H.; Grabherr, M.; Ramesh, A.; Scheerder, B.; Hijmans, J.M. Flexible Machine Learning Algorithms for Clinical Gait Assessment Tools. Sensors 2022, 22, 4957. https://doi.org/10.3390/s22134957

AMA Style

Greve C, Tam H, Grabherr M, Ramesh A, Scheerder B, Hijmans JM. Flexible Machine Learning Algorithms for Clinical Gait Assessment Tools. Sensors. 2022; 22(13):4957. https://doi.org/10.3390/s22134957

Chicago/Turabian Style

Greve, Christian, Hobey Tam, Manfred Grabherr, Aditya Ramesh, Bart Scheerder, and Juha M. Hijmans. 2022. "Flexible Machine Learning Algorithms for Clinical Gait Assessment Tools" Sensors 22, no. 13: 4957. https://doi.org/10.3390/s22134957

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