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Updated: Nov 18, 2025

Subject-specific Musculoskeletal Model for Studying Bone Strain During Dynamic Motion

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Published on: April 11, 2018

Hand Motion Capture from a 3D Leap Motion Controller for a Musculoskeletal Dynamic Simulation.

Robin Fonk¹, Sean Schneeweiss¹, Ulrich Simon¹

¹Scientific Computing Centre Ulm (UZWR), Ulm University, 89081 Ulm, Germany.

Sensors (Basel, Switzerland)

|February 11, 2021

Summary

This study introduces ROSE Motion, a tool integrating Leap Motion Controller (LMC) hand tracking data with AnyBody Modeling System (AMS) software. This streamlines musculoskeletal simulations by simplifying hand movement recording.

Keywords:

AMS anybody modeling system bvh hand motion leap motion controller motion capture musculoskeletal hand model range of motion

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Area of Science:

Biomechanics
Human Motion Analysis
Computational Modeling

Background:

Musculoskeletal modeling software like AnyBody Modeling System (AMS) requires complex motion data input.
Current methods for recording individual movements for AMS are time-consuming.
Contactless motion tracking, such as the Leap Motion Controller (LMC), shows potential for capturing hand movement data.

Purpose of the Study:

To integrate LMC hand motion data into the AMS to simplify and expedite the recording process.
To develop a novel interface for seamless data transfer between LMC and AMS.
To enhance the efficiency of musculoskeletal simulations involving hand and arm movements.

Main Methods:

Development of a Python-based interface named ROSE Motion to connect LMC and AMS.
Utilizing LMC for contactless hand motion capture.
Saving motion data as Biovision Hierarchy (BVH) and AnyScript vector files for AMS import.
Employing AnyPyTools for automated simulation parameter setting, calculation initiation, and results retrieval.

Main Results:

The developed tool, ROSE Motion, enables rapid and user-friendly recording of elbow, hand, and finger movements.
Features include animation, motion editing, exporting, and remote control of AMS simulations.
While LMC data has limitations in correctness, the system offers faster setup and intuitive editing compared to marker-based methods.

Conclusions:

The integration of LMC data into AMS via ROSE Motion significantly improves the efficiency of musculoskeletal motion recording.
Markerless motion capture systems, despite some accuracy limitations, present a viable and advantageous alternative to marker-based systems for specific applications.
This approach facilitates quicker analysis and presentation of musculoskeletal simulation results.