Full metadata record
| DC Field | Value | Language |
|---|---|---|
| dc.contributor | Department of Rehabilitation Sciences | en_US |
| dc.contributor.advisor | Fong, Kenneth (RS) | en_US |
| dc.creator | Lam, Wing Tung | - |
| dc.identifier.uri | https://theses.lib.polyu.edu.hk/handle/200/13291 | - |
| dc.language | English | en_US |
| dc.publisher | Hong Kong Polytechnic University | en_US |
| dc.rights | All rights reserved | en_US |
| dc.title | Effects of the application of markerless motion capture (MMC) technology for patients with stroke | en_US |
| dcterms.abstract | Markerless Motion Capture (MMC) technology has been developed to eliminate the need of attaching markers on the human body during motion capturing and analysis. One of the clinical conditions that MMC technology can be applied is in the patients with stroke - a population who usually requires continuous measurement on their motor performance in pre/post rehabilitation intervention. However, there remains questions on the reliability of the MMC technology for clinical application, and the benefits of it in providing clinical measurement for patients with stroke. Therefore, this thesis aimed to examine the application of MMC technology in the patients with stroke. | en_US |
| dcterms.abstract | Our systematic review and meta-analysis on the application of MMC technology in rehabilitation training revealed the potential for MMC systems to be used in telerehabilitation training program. Additionally, the review on the application of MMC systems in clinical measurement revealed that MMC system can analyze the movement kinematics of the disease populations, which suggested that they can serve as an alternative tool to measure the movement kinematic in these populations. | en_US |
| dcterms.abstract | We then conducted a pilot study that investigated the validity and reliability of a customized MMC system developed using iPad Pro with LiDAR scanner for the capturing of movement kinematics. The performance of measuring the active range of motion (AROM) and the angular waveform of the upper-limb-joint angles in functional tasks on healthy adults using the MMC system was examined. We found that the AROM measurements calculated by the MMC system had consistently smaller values than those measured by the goniometer. An MMC in iPad Pro system might not be able to replace conventional goniometry for clinical ROM measurements, but it is still suggested for use in telerehabilitation for intra-subject measurements because of its good reliability and portability. | en_US |
| dcterms.abstract | We further investigated the application of MMC system in the measurement of both upper and lower limb kinematics in the stroke population, by examining the differences in the upper and lower limb joint angles between patients with stroke with different functional levels and their healthy counterparts in controlled and uncontrolled environments. Machine-learning models were also applied for classification of the functioning levels of the participants with stroke. We found significant differences between the upper limbs of the hemiplegic and non-hemiplegic sides of the stroke participants in most of the tasks. The four selected machine-learning models revealed ≥ 0.85 sensitivity in the stroke upper limb functional level classification. | en_US |
| dcterms.abstract | For the lower limb measurement, significant differences were found between the angle change of the hemiplegic and non-hemiplegic lower limb of the stroke participants in most of the selected task. Our result revealed that MMC system can be used to provide precise data to evaluate the upper and lower limb functional recovery of the patients with stroke. Our study hence supports the feasibility of applying MMC system in mobile device in measuring the upper and lower limb kinematics for evaluation of the limb function of the stroke population. Future directions of research including increasing of the usability of the MMC system using smartphone or tablets in telerehabilitation are suggested. | en_US |
| dcterms.extent | xxiii, 312 pages : color illustrations | en_US |
| dcterms.isPartOf | PolyU Electronic Theses | en_US |
| dcterms.issued | 2024 | en_US |
| dcterms.educationalLevel | Ph.D. | en_US |
| dcterms.educationalLevel | All Doctorate | en_US |
| dcterms.LCSH | Motor ability | en_US |
| dcterms.LCSH | Motor learning | en_US |
| dcterms.LCSH | Cerebrovascular disease -- Patients -- Rehabilitation | en_US |
| dcterms.LCSH | Hong Kong Polytechnic University -- Dissertations | en_US |
| dcterms.accessRights | open access | en_US |
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