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.


    Zugriff

    Download


    Exportieren, teilen und zitieren



    Titel :

    Flexible Machine Learning Algorithms for Clinical Gait Assessment Tools


    Beteiligte:
    Greve, Christian (Autor:in) / Tam, Hobey (Autor:in) / Grabherr, Manfred (Autor:in) / Ramesh, Aditya (Autor:in) / Scheerder, Bart (Autor:in) / Hijmans, Juha M. (Autor:in)

    Erscheinungsdatum :

    2022-01-01


    Anmerkungen:

    ISI:000822283300001



    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    Klassifikation :

    DDC:    629



    Flexible Machine Learning Algorithms for Clinical Gait Assessment Tools

    Greve, Christian / Tam, Hobey / Grabherr, Manfred et al. | BASE | 2022

    Freier Zugriff

    Automated Activity Recognition with Gait Positions Using Machine Learning Algorithms

    Ng, Yeuk Lam / Jiang, Xilin / Zhang, Yinghui et al. | BASE | 2019

    Freier Zugriff

    Gait stride length estimation using embedded machine learning

    Verbiest, Joeri R. / Bonnechere, Bruno / Saeys, Wim et al. | BASE | 2023

    Freier Zugriff

    Learning Human Gait

    Kasebzadeh, Parinaz | BASE | 2019

    Freier Zugriff

    Usage of Machine Learning Algorithms in Flexible Use of Airspace Concept

    Birdal, Emre Osman / Uzumcu, Serdar | IEEE | 2019