Flexible Machine Learning Algorithms for Clinical Gait Assessment Tools

Christian Greve*, Hobey Tam, Manfred Grabherr, Aditya Ramesh, Bart Scheerder, Juha Hijmans

*Bijbehorende auteur voor dit werk

OnderzoeksoutputAcademicpeer review

38 Downloads (Pure)


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.
Originele taal-2English
Aantal pagina's11
Nummer van het tijdschrift13
StatusPublished - 30-jun.-2022

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