Muscle synergies are consistent across level and uphill treadmill running

Saito, Akira; Tomita, Aya; Ando, Ryosuke; Watanabe, Kohei; Akima, Hiroshi

doi:10.1038/s41598-018-24332-z

Cited by 61 publications

(54 citation statements)

References 44 publications

(75 reference statements)

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“…Nonetheless, since NMF does not rely on the type of distribution (Gaussian, non-Gaussian) of muscle activation patterns, it is likely to be the most sensitive method for characterising neuro-related alterations in the PDF. We utilised three muscle synergies (based on>85% VAF) for all four factorisation methods, which is in line with previous investigations using NMF 20,61 , PCA 23 , ICA 29 and FA 27 for walking and running at different speeds. Further, our primary metrics (VAF, occurrence of agreement, and maximum dissimilarity) remained relatively unchanged when four ( Supplementary Fig.…”

Section: Discussionmentioning

confidence: 92%

Non-negative matrix factorisation is the most appropriate method for extraction of muscle synergies in walking and running

Rabbi

Pizzolato

Lloyd

et al. 2020

Sci Rep

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Muscle synergies provide a simple description of a complex motor control mechanism. Synergies are extracted from muscle activation patterns using factorisation methods. Despite the availability of several factorisation methods in the literature, the most appropriate method for muscle synergy extraction is currently unknown. In this study, we compared four muscle synergy extraction methods: non-negative matrix factorisation, principal component analysis, independent component analysis, and factor analysis. Probability distribution of muscle activation patterns were compared with the probability distribution of synergy excitation primitives obtained from the four factorisation methods. Muscle synergies extracted using non-negative matrix factorisation best matched the probability distribution of muscle activation patterns across different walking and running speeds. Non-negative matrix factorisation also best tracked changes in muscle activation patterns compared to the other factorisation methods. Our results suggest that non-negative matrix factorisation is the best factorisation method for identifying muscle synergies in dynamic tasks with different levels of muscle contraction. Despite decades of research, it remains unclear how the human central nervous system (CNS) coordinates activity of a large number of muscles during movement. Numerous studies suggest that the CNS activates muscles in groups to reduce the complexity required to control each individual muscle when performing a movement 1,2. The coordination of muscles activated in synchrony is commonly referred to as muscle synergy 3. Indirect evidence suggests that muscle synergies reside in the brainstem and/or spinal cord and follow a modular organization 4-6. Muscle synergies are regarded as low dimensional units that produce complex activation patterns for a group of muscles, typically recorded via electromyography (EMG), during performance of a task 4,6. Synergies can be observed at cortical 7 or spinal 8 levels, suggesting a high degree of cooperation within the CNS's structural hierarchy 9. Understanding the organisation of muscle synergies may help elucidate the neurological mechanisms that underpin a multitude of neurological conditions, including stroke 10-12 , cerebral palsy 13,14 , spinal cord injury 15 , and Parkinson's Disease 16. Factorisation methods use recorded and processed EMG signals, from here referred to as muscle activation patterns, to quantify muscle synergies. A number of different factorisation methods have been used to extract muscle synergies from muscle activation patterns during dynamic tasks. The four most commonly used factorisation methods reported in the literature between 1999-2018 are non-negative matrix factorisation (NMF) 13,14,17-20 (62.28%), principal component analysis (PCA) 21-24 (23.11%), independent component analysis (ICA) 25,26 (3.22%), and factor analysis (FA) 27,28 (2.15%) (Literature search in Supplementary material). Applying a factorisation method to a set of muscle activation patterns yields two compon...

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Section: Discussionmentioning

confidence: 92%

Non-negative matrix factorisation is the most appropriate method for extraction of muscle synergies in walking and running

Rabbi

Pizzolato

Lloyd

et al. 2020

Sci Rep

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“…Perturbation has to be intended as a change of movement, as reported in the Oxford dictionary. Perturbations can be [83,84,89,90,92,93,95,96,111,113,116,118,123,126,127,129,130,133,136] Resistance training or weightlifting 13 [78,80,81,85,86,94,97,102,103,105,108,125,128] 11 Applied Bionics and Biomechanics used to uncover motor control processes that under unperturbed circumstances would not be available for observation [162]. Of the six studies that dealt with perturbations, four have been published after March 2017, indicating an increasing interest in the topic by the sport science community [97,102,103,110,121,136].…”

Section: Sport Application With Emgmentioning

confidence: 99%

Sport Biomechanics Applications Using Inertial, Force, and EMG Sensors: A Literature Overview

Taborri

Keogh

Kos

et al. 2020

Applied Bionics and Biomechanics

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In the last few decades, a number of technological developments have advanced the spread of wearable sensors for the assessment of human motion. These sensors have been also developed to assess athletes’ performance, providing useful guidelines for coaching, as well as for injury prevention. The data from these sensors provides key performance outcomes as well as more detailed kinematic, kinetic, and electromyographic data that provides insight into how the performance was obtained. From this perspective, inertial sensors, force sensors, and electromyography appear to be the most appropriate wearable sensors to use. Several studies were conducted to verify the feasibility of using wearable sensors for sport applications by using both commercially available and customized sensors. The present study seeks to provide an overview of sport biomechanics applications found from recent literature using wearable sensors, highlighting some information related to the used sensors and analysis methods. From the literature review results, it appears that inertial sensors are the most widespread sensors for assessing athletes’ performance; however, there still exist applications for force sensors and electromyography in this context. The main sport assessed in the studies was running, even though the range of sports examined was quite high. The provided overview can be useful for researchers, athletes, and coaches to understand the technologies currently available for sport performance assessment.

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“…To test the second hypothesis on whether muscle synergies were altered or dissimilar in people with FSHD, Pearson's correlation coefficients were used to quantify synergy weight and zero-lag correlation coefficients to quantify synergy coefficient similarities (: 0.025) [34]. Correlation coefficients values were calculated only for significantly similar synergy weights to minimize type I errors.…”

Section: E Statistical Analysismentioning

confidence: 99%

Superficial Shoulder Muscle Synergy Analysis in Facioscapulohumeral Dystrophy During Humeral Elevation Tasks

Essers

Peters

Meijer

et al. 2019

IEEE Trans. Neural Syst. Rehabil. Eng.

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Muscle synergies are consistent across level and uphill treadmill running

Cited by 61 publications

References 44 publications

Non-negative matrix factorisation is the most appropriate method for extraction of muscle synergies in walking and running

Non-negative matrix factorisation is the most appropriate method for extraction of muscle synergies in walking and running

Sport Biomechanics Applications Using Inertial, Force, and EMG Sensors: A Literature Overview

Superficial Shoulder Muscle Synergy Analysis in Facioscapulohumeral Dystrophy During Humeral Elevation Tasks

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