Abstract
Motor disability due to stroke, accident, or other illnesses in many cases can be partially or fully recovered with guided and/or prescribed physiotherapy. Moreover, due to the nature of the problem, in most of the case, the treatment is needed to be continued for years. Thus, one of the main challenges in this is the availability of an expert physiotherapist to guide the treatment. Most of the time, after initial guided physiotherapy sessions, patients are prescribed exercises to practice at home by themselves. Patients may be able to perform the task at home; however, the expert is present during home sessions, hence, no performance assessment. In this paper, we present a prototype system to resolve this problem using 3D sensor technology: Microsoft Kinect v2. An expert can record an exercise by performing it in front of the 3D camera and provide the prescription (how many times and when the exercise should be performed) on the system. At home, the patient performs the exercise in front of our Kinect-powered system which guides the patient through visual cues on the monitor. In this initial study, five occupational arthritis exercises on the right arm were recorded. We matched these exercises with 15 healthy volunteers (10 male, 5 female) standing 1.5 ms from the system. To overcome different body shapes, we used geometric coordinates from recorded frames and recreated the visual cues. Our system matched the exercises with uncertainty of 10px (2% of depth resolution) and volunteers adapted to screen instructions with minimum supervision.
Supported by a grant from the ICT Division of the People’s Republic of Bangladesh Government.
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Acknowledgement
We thank all the 15 volunteers who donated their time for this study. This work is part of the Human Activity Recognition project for partially disabled patients seeking rehabilitation. It is supported by a grant from the ICT Division of the People’s Republic of Bangladesh Government.
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Rabbani, S.B., Ali, A.A., Amin, M.A. (2021). Using Microsoft Kinect V2 for Custom Upper-Limb Rehabilitation Exercises. In: Abraham, A., et al. Proceedings of the 12th International Conference on Soft Computing and Pattern Recognition (SoCPaR 2020). SoCPaR 2020. Advances in Intelligent Systems and Computing, vol 1383. Springer, Cham. https://doi.org/10.1007/978-3-030-73689-7_49
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DOI: https://doi.org/10.1007/978-3-030-73689-7_49
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