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Predicting muscle forces measurements from kinematics data using kinect in stroke rehabilitation

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Abstract

Muscle strength is mostly measured by wearable devices. However, wearing such devices is a tedious, unpleasant, and sometimes impossible task for stroke patients. In this paper, a mathematical model is proposed to estimate the strength of the upper limb muscles of a stroke patient by using Microsoft Kinect sensor. A prototype exergame is designed and developed to mimic real post-stroke rehabilitation exercises. Least-square regression matrix is used to find the relation between the kinematics of the upper limb and the strength of the corresponding muscles. Kinect sensor is used along with a force sensing resistors (FSR) glove and two straps to collect both, real-time upper limb joints data and the strength of muscles of the subjects while they are performing the exercises. The prototype of this system is tested on five stroke patients and eight healthy subjects. Results show that there is no statistically significant difference between the measured and the estimated values of the upper-limb muscles of the stroke patients. Thus, the proposed method is useful in estimating the strength of the muscles of stroke patient without the need to wear any devices.

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Authors and Affiliations

  1. Multimedia Communication Research Laboratory, University of Ottawa, 800 King Edward, Ottawa, ON, k1n6n5, Canada

    Mohamad Hoda, Basim Hafidh & Abdulmotaleb El Saddik

  2. Perpuim Care Polyclinic, Doha, Qatar

    Yehya Hoda

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  1. Mohamad Hoda
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  2. Yehya Hoda
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  3. Basim Hafidh
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  4. Abdulmotaleb El Saddik
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Correspondence to Mohamad Hoda.

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Hoda, M., Hoda, Y., Hafidh, B. et al. Predicting muscle forces measurements from kinematics data using kinect in stroke rehabilitation. Multimed Tools Appl 77, 1885–1903 (2018). https://doi.org/10.1007/s11042-016-4274-5

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  • DOI: https://doi.org/10.1007/s11042-016-4274-5

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