Upper limb kinematic measurement using markerless motion capturing (MMC) in stroke survivors: A cross-sectional experimental study

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.

Authors

Institutions

Publication Details

Journal
Digital Health
Published
2025-05-01
DOI
https://doi.org/10.1177/20552076251342009
Citations
3
Primary Topic
Stroke Rehabilitation and Recovery
Type
article
Field-Weighted Citation Impact
3.08

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Upper limb kinematic measurement using markerless motion capturing (MMC) in stroke survivors: A cross-sectional experimental study

Chi‐Wen Chien, Kenneth N. K. Fong, Winnie W. T. Lam
3 citations
Digital Health
Stroke Rehabilitation and Recovery
3.08
article

Upper limb kinematic measurement using markerless motion capturing (MMC) in stroke survivors: A cross-sectional experimental study

Chi‐Wen Chien, Kenneth N. K. Fong, Winnie W. T. Lam
article en
3 citations

Abstract

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.

Digital HealthVol. 11
Hong Kong Polytechnic University (HK)
Research Grants Council, University Grants Committee
Good health and well-being
Openalex Percentile: Top 8%
Stroke Rehabilitation and Recovery
3.08
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.