ArticleSensors (Basel, Switzerland)2025
Optimisation and Comparison of Markerless and Marker-Based Motion Capture Methods for Hand and Finger Movement Analysis.
Article in Sensors (Basel, Switzerland), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- Markerless On-Device Detection of Compensatory Movement Patterns in Upper-Limb Rehabilitation Exercises from Monocular RGB Video: A Validation Study in Healthy Adults.Sensors (Basel, Switzerland) · 2026Article
- Towards routine biomechanical data collection in stroke rehabilitation: a usability comparison of IMU and markerless motion capture systems for functional upper-limb assessments.Journal of neuroengineering and rehabilitation · 2026Article
- Enhancing Work Efficiency and Safety Culture in the Food Industry Using Behavioral Patterns: A Video-Based Case Study from Poland.Foods (Basel, Switzerland) · 2026Article
- Interpretable and granular video-based quantification of motor characteristics from the finger-tapping test in Parkinson's disease.NPJ Parkinson's disease · 2026Article
- Video-based finger kinematics for degenerative cervical myelopathy: a smartphone-based computer vision approach for screening.European spine journal : official publication of the European Spine Society, the European Spinal Deformity Society, and the European Section of the Cervical Spine Research Society · 2026Article
- TrackStudio: An integrated toolkit for markerless tracking.PloS one · 2026Article
- Comparative Analysis of the Accuracy and Robustness of the Leap Motion Controller 2.Sensors (Basel, Switzerland) · 2025Article
- Validated low-cost standardized VICON configuration as a practical approach to estimating the minimal accuracy of a specific setup.Scientific reports · 2025Article
- Frailty-Focused Movement Monitoring: A Single-Camera System Using Joint Angles for Assessing Chair-Based Exercise Quality.Sensors (Basel, Switzerland) · 2025Article
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Authors and funding
4 authors.
Funding
Abstract
Ensuring the accurate tracking of hand and fingers movements is an ongoing challenge for upper limb rehabilitation assessment, as the high number of degrees of freedom and segments in the limited volume of the hand makes this a difficult task. The objective of this study is to evaluate the performance of two markerless approaches (the Leap Motion Controller and the Google MediaPipe API) in comparison to a marker-based one, and to improve the precision of the markerless methods by introducing additional data processing algorithms fusing multiple recording devices. Fifteen healthy participants were instructed to perform five distinct hand movements while being recorded by the three motion capture methods simultaneously. The captured movement data from each device was analyzed using a skeletal model of the hand through the inverse kinematics method of the OpenSim software. Finally, the root mean square errors of the angles formed by each finger segment were calculated for the markerless and marker-based motion capture methods to compare their accuracy. Our results indicate that the MediaPipe-based setup is more accurate than the Leap Motion Controller-based one (average root mean square error of 10.9° versus 14.7°), showing promising results for the use of markerless-based methods in clinical applications.
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Registered trials
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