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Modular Control of Human Movement During Running: An Open Access Data Set
Alessandro Santuz1,2, Antonis Ekizos1,2, Lars Janshen1
1Department of Training and Movement Sciences, Humboldt-Universität zu Berlin, Berlin, Germany.
Frontiers in Physiology
|November 14, 2018
Summary
This study introduces a large dataset of leg muscle activities during running, extracted using non-negative matrix factorization (NMF). The findings reveal four key muscle synergies that effectively represent different running phases, aiding motor control research.
Area of Science:
- * Neuroscience and Biomechanics: Investigating the neural control of human movement.
- * Computational Biology: Applying advanced algorithms to biological data analysis.
Background:
- * Human movement is controlled by the central nervous system, which likely simplifies motor control by organizing muscles into synergistic patterns.
- * Muscle synergies, proposed early in the 20th century, represent coordinated muscle activation patterns rather than individual muscle control.
- * Extracting muscle synergies from electromyography (EMG) signals has been limited by small sample sizes in previous studies.
Purpose of the Study:
- * To create and release a large, open-access dataset of lower limb EMG activity during treadmill running.
- * To provide the computational methods (non-negative matrix factorization) and results for extracting muscle synergies.
- * To facilitate broader research in human motor control, robotics, and sports science.
Main Methods:
- * Collected EMG data from 135 healthy adults (78 males, 57 females) performing treadmill running.
- * Utilized non-negative matrix factorization (NMF) to extract muscle synergies from 13 channels of ipsilateral EMG data.
- * Identified time-invariant muscle weightings (motor modules) and time-dependent activation coefficients (motor primitives).
Main Results:
- * Four muscle synergies were identified as sufficient to represent distinct phases of the running gait cycle.
- * These synergies effectively captured the complex coordination of the lower limb during running.
- * The extracted synergies provide a simplified yet comprehensive model of running motor control.
Conclusions:
- * The released dataset significantly expands the available data for studying human motor control, offering a robust foundation for future research.
- * This resource serves as a valuable benchmark for cross-disciplinary research in areas like musculoskeletal modeling, robotics, and clinical neuroscience.
- * The data and code can be used for educational purposes and to refine existing methods for muscle synergy extraction.
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