Similarity of muscle synergies extracted from the lower limb including the deep muscles between level and uphill treadmill walking

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

doi:10.1016/j.gaitpost.2017.10.007

Cited by 48 publications

(42 citation statements)

References 33 publications

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“…The extraction of four synergies from step ascent is in agreement with previous studies of locomotion in healthy adults that included a maximum of ten lower limb muscles [24,40]. An important aspect of this study is the inclusion of EMG signals obtained from 10 lower limb muscles, including the gluteus medius, gluteus maximus and the erector spinae.…”

Section: Discussionsupporting

confidence: 84%

“…These synergies are composed of groups of muscles that are assumed to be activated by a single neural command [23]. It is thought that the central nervous system employs this modular organization to reduce the large number of degrees of freedom inherent to the redundancy of the human musculoskeletal system [24], and to allow for flexible but accurate response selection during motor tasks [25]. However, some researchers have argued that modular recruitment of muscles might represent predetermined control strategies and could merely be an effect of task constraints or optimized performance criteria, rather than reflecting neural control strategies employed by the central nervous system [23,26].…”

Section: Introductionmentioning

confidence: 99%

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Age-Related Differences in Muscle Synergy Organization during Step Ascent at Different Heights and Directions

et al. 2020

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The aim of this study was to explore the underlying age-related differences in dynamic motor control during different step ascent conditions using muscle synergy analysis. Eleven older women (67.0 y ± 2.5) and ten young women (22.5 y ± 1.6) performed stepping in forward and lateral directions at step heights of 10, 20 and 30 cm. Surface electromyography was obtained from 10 lower limb and torso muscles. Non-negative matrix factorization was used to identify sets of (n) synergies across age groups and stepping conditions. In addition, variance accounted for (VAF) by the detected number of synergies was compared to assess complexity of motor control. Finally, correlation coefficients of muscle weightings and between-subject variability of the temporal activation patterns were calculated and compared between age groups and stepping conditions. Four synergies accounted for >85% VAF across age groups and stepping conditions. Age and step height showed a significant negative correlation with VAF during forward stepping but not lateral stepping, with lower VAF indicating higher synergy complexity. Muscle weightings showed higher similarity across step heights in older compared to young women. Neuromuscular control of young and community-dwelling older women could not be differentiated based on the number of synergies extracted. Additional analyses of synergy structure and complexity revealed subtle age- and step-height-related differences, indicating that older women rely on more complex neuromuscular control strategies.

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Section: Discussionsupporting

confidence: 84%

Section: Introductionmentioning

confidence: 99%

Age-Related Differences in Muscle Synergy Organization during Step Ascent at Different Heights and Directions

et al. 2020

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“…Saito et al . 40 also demonstrated that the shapes of synergy activation coefficients appeared similar between level and 10% uphill walking, with a few time-shift adjustments with changes in grade (i.e., <8.8% of step cycle). We therefore speculated that the temporal activation pattern of muscle synergy during running was preserved regarding changes in the slope of the ground surface.…”

Section: Discussionmentioning

confidence: 73%

Muscle synergies are consistent across level and uphill treadmill running

Saito

Tomita

Ando

et al. 2018

Sci Rep

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This study aimed to identify muscle synergies of the lower limb during treadmill running on level and inclined ground. Eight subjects ran on a treadmill at three speeds (2.5, 3.3, and 4.1 m/s) and two grades (level and 10% grade). Surface electromyographic (EMG) signals were recorded from 10 muscles of the lower limb, including deeper muscles such as vastus intermedius, adductor magnus, and adductor longus. Muscle synergies were extracted applying a non-negative matrix factorization algorithm, and relative co-activations across muscles and the temporal recruitment pattern were identified by muscle synergy vector and synergy activation coefficient, respectively. The scalar product between pairs of synergy vectors and synergy activation coefficients during level and uphill running conditions were analyzed as a similarity index, with values above 0.8 recognized as similar. Approximately 4 muscle synergies controlled the majority of variability in 10 EMGs during running, and were common between level and uphill conditions. At each running speed, inter-condition similarity was observed in synergy vector (r > 0.83) and synergy activation coefficients (r > 0.84) at each type of synergy. These results suggest that types of synergy are consistent between level and uphill running.

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“…There are several examples of studies employing factorization of EMG activity to study human locomotion. For several reasons, the most widespread locomotion type that has been studied is walking ( Ivanenko et al, 2004 ; Cappellini et al, 2006 ; Courtine et al, 2006 ; Clark et al, 2010 ; McGowan et al, 2010 ; Dominici et al, 2011 ; Allen and Neptune, 2012 ; Bolton and Misiaszek, 2012 ; Chvatal and Ting, 2012 , 2013 ; Lacquaniti et al, 2012 ; Oliveira et al, 2012 ; Rodriguez et al, 2013 ; Barroso et al, 2014 ; Maclellan et al, 2014 ; Routson et al, 2014 ; Coscia et al, 2015 ; Gonzalez-Vargas et al, 2015 ; Hagio et al, 2015 ; Licence et al, 2015 ; Martino et al, 2015 ; Nazifi et al, 2015 ; Tang et al, 2015 ; Buurke et al, 2016 ; Gui and Zhang, 2016 ; Kim et al, 2016 ; Lencioni et al, 2016 ; Meyer et al, 2016 ; Pérez-Nombela et al, 2016 ; Yokoyama et al, 2016 ; Allen et al, 2017 ; Janshen et al, 2017 ; Santuz et al, 2017a ; Shuman et al, 2017 ; Saito et al, 2018 ). Due to the easiness of examining this slow-speed type of locomotion, it is not a surprise that the majority of studies use walking as the main object of investigation.…”

Section: Discussionmentioning

confidence: 99%

Modular Control of Human Movement During Running: An Open Access Data Set

et al. 2018

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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).

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Similarity of muscle synergies extracted from the lower limb including the deep muscles between level and uphill treadmill walking

Cited by 48 publications

References 33 publications

Age-Related Differences in Muscle Synergy Organization during Step Ascent at Different Heights and Directions

Age-Related Differences in Muscle Synergy Organization during Step Ascent at Different Heights and Directions

Muscle synergies are consistent across level and uphill treadmill running

Modular Control of Human Movement During Running: An Open Access Data Set

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