Trustworthy AI Assessment of Quality of movements in Trunk-control Rehabilitation exercises for children
Michael Joseph Sherman, Joseph (TU Delft Mechanical, Maritime and Materials Engineering)
Marchal Crespo, L. (mentor)
Degree granting institution
Zgonnikov, A. (graduation committee)
Pangalila, Robert (graduation committee)
Delft University of Technology
Neurological disorders in the nervous and neuromuscular systems affect approximately 260 million people annually and among these 255 million would benefit from rehabilitation . Patients with neurological disorders usually require multi-dimensional rehabilitation, involving physical, cognitive, psychological, and medical help. Children with trunk control problems arising due to some of these neurological disorders also require such multi-dimensional rehabilitation. A major part of this is administered to the patient through the activities of a physiotherapist in the clinical context. But the limited number of physiotherapists result in exercises often being prescribed for patients as in-home rehabilitation. During in-home rehabilitation, the patient and the primary care-giver may not be able to comply with the prescription without feedback from a physiotherapist.
To address this challenge, this paper proposes an automated method for assessing movement quality of children during trunk control rehabilitation exercises. We adopted a Human-centered AI approach to the development of our system. We identified the needs of physiotherapists for assessing patient’s functional abilities through semi-structured interviews with six physiotherapists. As a result, we co-designed and developed an artificially Intelligent decision support system that automatically assesses the quality of motion. We created a trunk-control rehabilitation exercise movement dataset based on a protocol co-designed by the authors and the physiotherapists. The data was collected
from 15 typically developing children (mean age 7 years, range 4–10 years) using a ZED-mini stereo-camera and the quality scores as ground-truth were obtained from a physiotherapist. The exercises involved reaching targets kept on a table in front
while being seated away from the table on a stool. We investigated the performance of Random Convolutional Kernel transform and XCM, two state-of-the-art multivariate
time-series classification algorithms on this dataset and achieved a quality prediction f1-score of 65% on the test dataset and similar promising results on the detection of compensatory movements in the exercise motion. In addition, to increase the trust-worthiness of our AI solution, we have provided explanations on the predictions of the black-box algorithms, which can aid the users of the system to understand the causal relationships between the input and output to the AI algorithm.
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© 2022 Joseph Michael Joseph Sherman