Flexible Machine Learning Algorithms for Clinical Gait Assessment Tools

Greve, ChristianUniv Groningen, Univ Med Ctr Groningen, Dept Rehabil Med, NL-9713 GZ Groningen, Netherlands.;Univ Groningen, Univ Med Ctr Groningen, Dept Human Movement Sci, NL-9713 GZ Groningen, Netherlands. (författare)

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.

TEKNIK OCH TEKNOLOGIER Medicinteknik Annan medicinteknik hsv//swe
ENGINEERING AND TECHNOLOGY Medical Engineering Other Medical Engineering hsv//eng
clinical gait analysis
gait partitioning
machine learning
wearables
inertial measurement units
sensors
deep neural networks
reinforcement learning

Tam, HobeyOro Muscles BV, NL-9715 CJ Groningen, Netherlands. (författare)
Grabherr, ManfredUppsala universitet,Institutionen för medicinsk biokemi och mikrobiologi,Oro Muscles BV, NL-9715 CJ Groningen, Netherlands.(Swepub:uu)mangr224 (författare)
Ramesh, AdityaUniv Groningen, Univ Med Ctr Groningen, Dept Biomed Engn, NL-9713 GZ Groningen, Netherlands. (författare)
Scheerder, BartUniv Groningen, Univ Med Ctr Groningen, Ctr Dev & Innovat CDI, NL-9713 GZ Groningen, Netherlands.;Univ Groningen, Univ Med Ctr Groningen, Data Sci Ctr Hlth Dash, NL-9713 GZ Groningen, Netherlands. (författare)
Hijmans, Juha M.Univ Groningen, Univ Med Ctr Groningen, Dept Rehabil Med, NL-9713 GZ Groningen, Netherlands. (författare)
Univ Groningen, Univ Med Ctr Groningen, Dept Rehabil Med, NL-9713 GZ Groningen, Netherlands.;Univ Groningen, Univ Med Ctr Groningen, Dept Human Movement Sci, NL-9713 GZ Groningen, Netherlands.Oro Muscles BV, NL-9715 CJ Groningen, Netherlands. (creator_code:org_t)

Av författaren/redakt...: Greve, Christian; Tam, Hobey; Grabherr, Manfre ...; Ramesh, Aditya; Scheerder, Bart; Hijmans, Juha M.

Om ämnet

TEKNIK OCH TEKNOLOGIER: TEKNIK OCH TEKNO ...; och Medicinteknik; och Annan medicintek ...

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