Muscle activation patterns and muscle synergies reflect different modes of coordination during upper extremity movement

Chen, Xiaoling; Dong, Xiaojiao; Feng, Yange; Jiao, Yuntao; J, Yu; Yan, Shuicheng; Li, Xinxin; Zhang, Lijie; Xie, Ping

doi:10.3389/fnhum.2022.912440

Cited by 7 publications

(5 citation statements)

References 46 publications

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“…According to the analysis, the ED, ECR, ECU and B muscles are specific synergy modules of WE, while the FCR, PL and FDS are specific synergy modules of WF. BB was a shared-muscle synergy module, which supports wrist flexion and extension and maintains the stability of the upper limb [18]; this conclusion is consistent with the results of studies on muscle activation and muscle synergism in different movement modes of the upper limb [40]. It was found that the action potentials used to produce various movements were different.…”

Section: Shared and Specific Synergysupporting

confidence: 85%

“…For the feature analysis of muscle coordination, previous studies mainly extracted and analyzed the spatio-temporal features of muscle coordination based on non-negative matrix decomposition. Xiao L et al introduced the non-negative matrix factorization (NMF) method to explore the muscle activation patterns and synergy structure under six types of movements [40]. De Marchis C et al analyzed the distribution of neural actuations among muscles that work together during the execution of a free-pedal task based on non-negative matrix factorization [17].…”

Section: Space-frequency-time Characteristics Of Muscle Synergymentioning

confidence: 99%

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Muscle Synergy during Wrist Movements Based on Non-Negative Tucker Decomposition

Chen,

Feng,

Chang

et al. 2024

Sensors

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Modular control of the muscle, which is called muscle synergy, simplifies control of the movement by the central nervous system. The purpose of this study was to explore the synergy in both the frequency and movement domains based on the non-negative Tucker decomposition (NTD) method. Surface electromyography (sEMG) data of 8 upper limb muscles in 10 healthy subjects under wrist flexion (WF) and wrist extension (WE) were recorded. NTD was selected for exploring the multi-domain muscle synergy from the sEMG data. The results showed two synergistic flexor pairs, Palmaris longus–Flexor Digitorum Superficialis (PL-FDS) and Extensor Carpi Radialis–Flexor Carpi Radialis (ECR-FCR), in the WF stage. Their spectral components are mainly in the respective bands 0–20 Hz and 25–50 Hz. And the spectral components of two extensor pairs, Extensor Digitorum–Extensor Carpi Ulnar (ED-ECU) and Extensor Carpi Radialis–Brachioradialis (ECR-B), are mainly in the respective bands 0–20 Hz and 7–45 Hz in the WE stage. Additionally, further analysis showed that the Biceps Brachii (BB) muscle was a shared muscle synergy module of the WE and WF stage, while the flexor muscles FCR, PL and FDS were the specific synergy modules of the WF stage, and the extensor muscles ED, ECU, ECR and B were the specific synergy modules of the WE stage. This study showed that NTD is a meaningful method to explore the multi-domain synergistic characteristics of multi-channel sEMG signals. The results can help us to better understand the frequency features of muscle synergy and shared and specific synergies, and expand the study perspective related to motor control in the nervous system.

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Section: Shared and Specific Synergysupporting

confidence: 85%

Section: Space-frequency-time Characteristics Of Muscle Synergymentioning

confidence: 99%

Muscle Synergy during Wrist Movements Based on Non-Negative Tucker Decomposition

Chen,

Feng,

Chang

et al. 2024

Sensors

Self Cite

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“…The preprocessing of sEMG signals involved several steps. First, a Butterworth high-pass filter with a cutoff frequency of 40 Hz was applied to the acquired sEMG signals ( Chen et al, 2023 ). Next, a 50 Hz notch filter was used to remove powerline interference.…”

Section: Methodsmentioning

confidence: 99%

“… Tang et al (2014) applied Pearson correlation analysis on muscle synergy among different healthy subjects executing similar tasks revealed a correlation coefficient of up to 0.85. Chen et al (2023) identified shared and specific synergies in six upper limb actions, forming a basis for the muscle synergy theory. Brambilla and Scano (2022) utilized experimental and simulated data to investigate the influence of the number of muscles on the structure and quantity of synergies.…”

Section: Introductionmentioning

confidence: 98%

Quantitative evaluation of motion compensation in post-stroke rehabilitation training based on muscle synergy

Liu,

Li,

Zhang

et al. 2024

Front. Bioeng. Biotechnol.

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Introduction: Stroke is the second leading cause of death globally and a primary factor contributing to disability. Unilateral limb motor impairment caused by stroke is the most common scenario. The bilateral movement pattern plays a crucial role in assisting stroke survivors on the affected side to relearn lost skills. However, motion compensation often lead to decreased coordination between the limbs on both sides. Furthermore, muscle fatigue resulting from imbalanced force exertion on both sides of the limbs can also impact the rehabilitation outcomes.Method: In this study, an assessment method based on muscle synergy indicators was proposed to objectively quantify the impact of motion compensation issues on rehabilitation outcomes. Muscle synergy describes the body’s neuromuscular control mechanism, representing the coordinated activation of multiple muscles during movement. 8 post-stroke hemiplegia patients and 8 healthy subjects participated in this study. During hand-cycling tasks with different resistance levels, surface electromyography signals were synchronously collected from these participants before and after fatigue. Additionally, a simulated compensation experiment was set up for healthy participants to mimic various hemiparetic states observed in patients.Results and discussion: Synergy symmetry and synergy fusion were chosen as potential indicators for assessing motion compensation. The experimental results indicate significant differences in synergy symmetry and fusion levels between the healthy control group and the patient group (p ≤ 0.05), as well as between the healthy control group and the compensation group. Moreover, the analysis across different resistance levels showed no significant variations in the assessed indicators (p > 0.05), suggesting the utility of synergy symmetry and fusion indicators for the quantitative evaluation of compensation behaviors. Although muscle fatigue did not significantly alter the symmetry and fusion levels of bilateral synergies (p > 0.05), it did reduce the synergy repeatability across adjacent movement cycles, compromising movement stability and hindering patient recovery. Based on synergy symmetry and fusion indicators, the degree of bilateral motion compensation in patients can be quantitatively assessed, providing personalized recommendations for rehabilitation training and enhancing its effectiveness.

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“…Considering the high number of degrees of freedom, it would cause an extremely heavy burden on the CNS if the muscles were individually controlled. One common opinion proposes that the CNS controls the muscles through a linear combination of neuromotor building blocks named muscle synergies to reduce the computational complexity [1][2][3][4][5][6][7]. Muscle synergies are defined as sets of muscles coordinately activated that work as a fixed combination [8][9][10][11][12].…”

Section: Introductionmentioning

confidence: 99%

Rectified Latent Variable Model-Based EMG Factorization of Inhibitory Muscle Synergy Components Related to Aging, Expertise and Force–Tempo Variations

Huang,

Guo,

Xie

et al. 2024

Sensors

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Muscle synergy has been widely acknowledged as a possible strategy of neuromotor control, but current research has ignored the potential inhibitory components in muscle synergies. Our study aims to identify and characterize the inhibitory components within motor modules derived from electromyography (EMG), investigate the impact of aging and motor expertise on these components, and better understand the nervous system’s adaptions to varying task demands. We utilized a rectified latent variable model (RLVM) to factorize motor modules with inhibitory components from EMG signals recorded from ten expert pianists when they played scales and pieces at different tempo–force combinations. We found that older participants showed a higher proportion of inhibitory components compared with the younger group. Senior experts had a higher proportion of inhibitory components on the left hand, and most inhibitory components became less negative with increased tempo or decreased force. Our results demonstrated that the inhibitory components in muscle synergies could be shaped by aging and expertise, and also took part in motor control for adapting to different conditions in complex tasks.

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Muscle activation patterns and muscle synergies reflect different modes of coordination during upper extremity movement

Cited by 7 publications

References 46 publications

Muscle Synergy during Wrist Movements Based on Non-Negative Tucker Decomposition

Muscle Synergy during Wrist Movements Based on Non-Negative Tucker Decomposition

Quantitative evaluation of motion compensation in post-stroke rehabilitation training based on muscle synergy

Rectified Latent Variable Model-Based EMG Factorization of Inhibitory Muscle Synergy Components Related to Aging, Expertise and Force–Tempo Variations

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