Santuz, A, Ekizos, A, Janshen, L, Mersmann, F, Bohm, S, Baltzopoulos, V and Arampatzis, A (2018) Modular control of human movement during running: An open access data set. Frontiers in Physiology, 9. ISSN 1664-042X

Preview

Text Modular control of human movement during running An open access data set.pdf - Published Version Available under License Creative Commons Attribution. Download (3MB) | Preview

Abstract

The human body is an outstandingly complex machine including around 1000 muscles and joints acting synergistically. Yet, the coordination of the enormous amount of degrees of freedom needed for movement is mastered by our one brain and spinal cord. The idea that some synergistic neural components of movement exist was already suggested at the beginning of the 20th century. Since then, it has been widely accepted that the central nervous system might simplify the production of movement by avoiding the control of each muscle individually. Instead, it might be controlling muscles in common patterns that have been called muscle synergies. Only with the advent of modern computational methods and hardware it has been possible to numerically extract synergies from electromyography (EMG) signals. However, typical experimental setups do not include a big number of individuals, with common sample sizes of 5 to 20 participants. With this study, we make publicly available a set of EMG activities recorded during treadmill running from the right lower limb of 135 healthy and young adults (78 males and 57 females). Moreover, we include in this open access data set the code used to extract synergies from EMG data using non-negative matrix factorization (NMF) and the relative outcomes. Muscle synergies, containing the time-invariant muscle weightings (motor modules) and the time-dependent activation coefficients (motor primitives), were extracted from 13 ipsilateral EMG activities using NMF. Four synergies were enough to describe as many gait cycle phases during running: weight acceptance, propulsion, early swing, and late swing. We foresee many possible applications of our data that we can summarize in three key points. First, it can be a prime source for broadening the representation of human motor control due to the big sample size. Second, it could serve as a benchmark for scientists from multiple disciplines such as musculoskeletal modeling, robotics, clinical neuroscience, sport science, etc. Third, the data set could be used both to train students or to support established scientists in the perfection of current muscle synergies extraction methods. All the data is available at Zenodo (doi: 10.5281/zenodo.1254380). © 2018 Frontiers Media S.A.All right reserved.

Item Type:	Article
Additional Information:	Santuz A, Ekizos A, Janshen L, Mersmann F, Bohm S, Baltzopoulos V and Arampatzis A (2018) Modular Control of Human Movement During Running: An Open Access Data Set. Front. Physiol. 9:1509. doi: 10.3389/fphys.2018.01509
Subjects:	R Medicine > RC Internal medicine > RC1200 Sports Medicine
Divisions:	Sport & Exercise Sciences
Publisher:	Frontiers Media
Date Deposited:	24 Jan 2019 09:51
Last Modified:	04 Sep 2021 02:02
DOI or ID number:	10.3389/fphys.2018.01509
URI:	https://researchonline.ljmu.ac.uk/id/eprint/10000

View Item