Dong, S. orcid.org/0000-0003-4782-5934, Gallagher, J. orcid.org/0000-0002-3341-175X, Jackson, A. et al. (1 more author) (2023) The Use of Kinematic Features in Evaluating Upper Limb Motor Function Learning Progress Based on Machine Learning. In: 2023 International Conference on Rehabilitation Robotics (ICORR). 2023 International Conference on Rehabilitation Robotics (ICORR), 24-28 Sep 2023, Singapore. Institute of Electrical and Electronics Engineers (IEEE)
Abstract
Evaluating progress throughout a patient's rehabilitation process helps choose effective treatment and formulate personalised and evidence-based rehabilitation interventions. The evaluation process is difficult due to the limitations of current clinical assessments. They lack the ability to reflect sensitive changes continuously throughout the rehabilitation process. Kinematic features have been extracted from individual's movement to address this problem due to their sensitivity and continuity. However, choosing appropriate kinematic features for rehabilitation evaluation has always been challenging. This paper exploits the application of kinematic features to classify movement patterns and movement qualities. 12 kinematic features were firstly extracted from a 7-segment triangle pattern of motion to monitor the learning progress with more numbers of drawing attempts. A statistical analysis was then conducted to compare the selected kinematic features with the clinically validated normalised jerk. Two supervised machine learning models were finally developed to classify movement patterns and movement qualities based on the selected kinematic features. The study was based on data recorded from 14 participants using a single position sensor. 6 kinematic features were able to reflect sensitive changes during the experiment and all kinematic features contributed to the classification tasks. Consistent with the literature, the results indicated that features based on movement velocity were the most beneficial in the classification tasks.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Keywords: | Support vector machines, Tracking, Kinematics, Machine learning, Feature extraction, Robot sensing systems, Mice |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Mechanical Engineering (Leeds) > Institute of Engineering Systems and Design (iESD) (Leeds) The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Mechanical Engineering (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 07 Aug 2024 13:56 |
Last Modified: | 07 Aug 2024 13:56 |
Status: | Published |
Publisher: | Institute of Electrical and Electronics Engineers (IEEE) |
Identification Number: | 10.1109/icorr58425.2023.10304807 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:215814 |