Digital health · 2025
Upper limb kinematic measurement using markerless motion capturing (MMC) in stroke survivors: A cross-sectional experimental study.
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Lam Winnie Wt, Fong Kenneth Nk, Chien Chi-Wen
Аннотация
INTRODUCTION: Markerless motion capture (MMC) technology has emerged as a clinical tool to assess the physical performance of patients. This study evaluates: (a) differences in upper limb joint angles between stroke survivors with different functional levels and their healthy counterparts in controlled indoor and uncontrolled outdoor environments; and (b) the relationship between the kinematic information obtained by the MMC system and the scores of manual motor assessments.
METHODS: A customized MMC system using an iPad Pro captured the participants' movements. Stroke survivors underwent three upper limb assessments and performed seven sets of upper limb tasks with their non-affected side, followed by their affected side. Healthy participants performed the same tasks with their dominant and non-dominant sides. The sensitivity and specificity of the machine models were calculated for classifying upper limb motor function levels using kinematic data from the MMC system.
RESULTS: Fifty stroke survivors and 49 healthy adults were recruited. Significant differences were found between the affected and non-affected sides of stroke participants in most tasks. Significant positive correlations were found between the results of the manual motor assessments and most of the kinematic parameters. The results of the four selected machine learning models revealed ≥0.85 sensitivity in the stroke upper limb functional level classification.
CONCLUSION: The MMC system and machine learning algorithms provide accurate data for evaluating upper limb recovery in stroke survivors. Further research is needed to explore the use of the MMC system by stroke survivors at home during remote therapy.
METHODS: A customized MMC system using an iPad Pro captured the participants' movements. Stroke survivors underwent three upper limb assessments and performed seven sets of upper limb tasks with their non-affected side, followed by their affected side. Healthy participants performed the same tasks with their dominant and non-dominant sides. The sensitivity and specificity of the machine models were calculated for classifying upper limb motor function levels using kinematic data from the MMC system.
RESULTS: Fifty stroke survivors and 49 healthy adults were recruited. Significant differences were found between the affected and non-affected sides of stroke participants in most tasks. Significant positive correlations were found between the results of the manual motor assessments and most of the kinematic parameters. The results of the four selected machine learning models revealed ≥0.85 sensitivity in the stroke upper limb functional level classification.
CONCLUSION: The MMC system and machine learning algorithms provide accurate data for evaluating upper limb recovery in stroke survivors. Further research is needed to explore the use of the MMC system by stroke survivors at home during remote therapy.