MOtoNMS: A MATLAB toolbox to process motion data for neuromusculoskeletal modeling and simulation.

Area of Science:

• Biomechanics and Movement Science
• Computational Biology and Bioinformatics
• Rehabilitation Engineering

Background:

• Neuromusculoskeletal modeling and simulation are crucial for understanding human movement dynamics.
• Clinical adoption of these methods is hindered by a lack of robust tools for pre-processing experimental movement data.
• Existing tools often lack the flexibility and user-friendliness required for seamless integration into clinical workflows.

Purpose of the Study:

• To present MOtoNMS (MATLAB Motion data elaboration Toolbox for NeuroMusculoSkeletal applications), a freely available toolbox designed to address the need for efficient pre-processing of experimental movement data.
• To facilitate the translation of neuromusculoskeletal modeling techniques into clinical practice by providing a user-friendly and extensible solution.
• To generate standardized input data compatible with popular neuromusculoskeletal modeling software like OpenSim and CEINMS.

Main Methods:

• MOtoNMS processes experimental data from various motion analysis devices.
• It implements essential pre-processing steps with a modular architecture for easy extension.
• User-friendly graphical interfaces allow laboratory configuration and processing setup without advanced programming skills.
• Configuration settings can be saved for reproducible results.

Main Results:

• MOtoNMS successfully processes experimental motion data, generating consistent inputs for neuromusculoskeletal modeling software (OpenSim, CEINMS).
• Data processed from four different institutions, using varied instrumentation and procedures, yielded consistent outputs, demonstrating MOtoNMS's robustness.
• The toolbox supports multiple motion analysis devices and integrates common pre-processing pipelines.

Conclusions:

• MOtoNMS effectively bridges the gap between motion analysis data acquisition and neuromusculoskeletal modeling.
• Its features, including multi-device support, comprehensive pre-processing, extensibility, intuitive interfaces, and free availability, are expected to accelerate the clinical application of neuromusculoskeletal methods.
• The toolbox enhances the practical utility of neuromusculoskeletal modeling in both research and clinical settings.