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Related Experiment Video

Updated: May 25, 2025

Estimation of Contact Regions Between Hands and Objects During Human Multi-Digit Grasping

Estimation of Contact Regions Between Hands and Objects During Human Multi-Digit Grasping

Published on: April 21, 2023

Optimisation and Comparison of Markerless and Marker-Based Motion Capture Methods for Hand and Finger Movement

Valentin Maggioni¹, Christine Azevedo-Coste¹, Sam Durand¹

¹Contrôle Artificiel de Mouvements et de Neuroprothèses Intuitives (CAMIN), Institut National de Recherche en Informatique et en Automatique (INRIA), Centre d'Université Côte d'Azur, Université de Montpellier, 34090 Montpellier, France.

Sensors (Basel, Switzerland)

|February 26, 2025

Summary

Markerless motion capture for hand and finger movement tracking shows promise in rehabilitation. Google MediaPipe API demonstrated higher accuracy than Leap Motion Controller, suggesting potential for clinical applications.

Keywords:

ecological movements hand and finger kinematics markerless motion capture skeletal model

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Last Updated: May 25, 2025

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Published on: January 15, 2018

Area of Science:

Biomedical Engineering
Rehabilitation Technology
Human Movement Analysis

Background:

Accurate tracking of hand and finger movements is crucial for upper limb rehabilitation assessment.
The complexity of hand anatomy presents challenges for precise motion capture.

Purpose of the Study:

To evaluate the performance of markerless motion capture methods (Leap Motion Controller, Google MediaPipe API) against a marker-based approach.
To enhance markerless method precision by integrating data processing algorithms and multiple recording devices.

Main Methods:

Fifteen healthy participants performed five distinct hand movements.
Simultaneous recording using Leap Motion Controller, Google MediaPipe API, and a marker-based system.
Analysis via skeletal hand model and inverse kinematics in OpenSim software.

Main Results:

Google MediaPipe API achieved higher accuracy (average RMSE 10.9°) compared to Leap Motion Controller (average RMSE 14.7°).
Markerless methods, particularly MediaPipe, showed promising accuracy for clinical use.

Conclusions:

Markerless motion capture, especially using Google MediaPipe API, offers a viable and accurate alternative for assessing hand and finger movements in clinical settings.
Further development in data processing can improve the precision of markerless systems for rehabilitation.