Neurological disorders in the nervous and neuromuscular systems affect approximately 260 million people annually and among these 255 million would benefit from rehabilitation [4]. 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.