Liu, Y, Liu, J, Ai, L et al. (3 more authors) (2019) Objective Evaluation of Hand ROM and Motion Quality based on Motion Capture and Brunnstrom Scale. In: 2019 IEEE/ASME INTERNATIONAL CONFERENCE ON ADVANCED INTELLIGENT MECHATRONICS (AIM). IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM), 08-12 Jul 2019, Hong Kong, China. , pp. 441-446. ISBN 978-1-7281-2493-3
Abstract
Evaluation of hand performance based on the collected data can be used to objectively and accurately assess the characteristics of hand motion quality for stroke patients. Current hand motion assessment is usually done by clinicians, which is heavily dependent on the therapist's experience and subjective judgment, the quality of motion is not quantifiable and intuitional. This paper proposes an objective evaluation method of the hand motion quality using the optical motion capture system combined with Brunnstrom criteria which is assessment a scale commonly used in clinics. The motion capture system is used to detect the maximum range of motion (ROM) of ten finger joints during the hand motion. A K-Nearest Neighbor algorithm is adapted to classify the hand movement quality levels of Brunnstrom evaluation criteria. Computer recognition of rehabilitation assessment of medical scale is realized, and it can intuitively and accurately reflect the user's hand movement state. Experiments were designed by taking into account the motion characteristics of Brunnstrom assessment, and the ROM of five common hand movements, including common flexion, coextension, thumb flexion, thumb-pinch, and spherical grasp were measured. A comparative study was conducted between the proposed method and the Brunnstrom scale, and the results verified this method's capability in evaluating the human hand motion quality, which has potential for rehabilitation evaluation of the hand motion of stroke patients and to provide the basis for the formulation of rehabilitation training programs.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. |
Keywords: | Joints , Read only memory , Thumb , Standards , Training , Classification algorithms , Roads |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Electronic & Electrical Engineering (Leeds) > Robotics, Autonomous Systems & Sensing (Leeds) |
Funding Information: | Funder Grant number EPSRC (Engineering and Physical Sciences Research Council) GCRF_IS_2017 |
Depositing User: | Symplectic Publications |
Date Deposited: | 29 Jun 2020 11:08 |
Last Modified: | 29 Jun 2020 11:08 |
Status: | Published |
Identification Number: | 10.1109/AIM.2019.8868793 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:162372 |