Modeling of the residual capability for people with severe motor disabilities: Analysis of hand posture

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Abstract

People with severe motor disabilities use mainly their residual motor capability for the use of technical aids, and for the control of input devices to technical aids. This paper describes our work on characterizing the motor capability of the upper arm for patients with severe motor disabilities. This work is a continuation of a project aimed at modeling the arm posture of quadriplegic patients using STS (Spatial Tracking System) and at analyzing the compensatory strategies developed by hemiplegic patients while accessing physical interfaces for technical aids [5]. Here we report work undertaken for analyzing the posture of the hand: we have developed two calibration methods for the Cyberglove and compare their utility and ergonomics in applications on patients with motor disabilities. The first type of calibration proceeds sequentially and takes into account one joint after the other (of the hand and each digit), whereas the second procedure is based on a few key postures calibrating several joints at once. To compare the precision of both methods, four healthy subjects participated in experiments using the Cyberglove. We show that the first type of calibration is more accurate but takes longer, whereas the second is less accurate but shorter. This trade-off might be acceptable for assessing the manual workspace in patients with motor disabilities. In particular, excessive muscular fatigue and limited dexterity are decisive factors for choosing the calibration by key postures in patients. We applied the calibration by key postures to three myopathic patients and individually quantified their restricted manual working space. © Springer-Verlag Berlin Heidelberg 2005.

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Kadouche, R., Mokhtari, M., & Maier, M. (2005). Modeling of the residual capability for people with severe motor disabilities: Analysis of hand posture. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 3538 LNAI, pp. 231–235). Springer Verlag. https://doi.org/10.1007/11527886_30

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