Comparison of muscle synergies extracted from both legs during cycling at different mechanical conditions

Esmaeili, Javad; Maleki, Ali

doi:10.1007/s13246-019-00767-0

Cited by 13 publications

(9 citation statements)

References 47 publications

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“…e lack of TRI can be observed in both mild-to-moderate and severe groups, which may indicate that stroke patients cannot keep their upper arm straight well. Furthermore, researchers found that different subjects at different mechanical conditions use the same motor control strategies in cycling and stance [46,47]. In our study, we found similar result that the muscle synergy within the control was relatively similar to each other, as the template shown in Figure 4.…”

Section: Muscle Synergy Analysissupporting

confidence: 81%

Motor Function Assessment of Upper Limb in Stroke Patients

Pan

Huang

Jin

et al. 2021

Journal of Healthcare Engineering

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Background. Quantitative assessment of motor function is extremely important for poststroke patients as it can be used to develop personalized treatment strategies. This study aimed to propose an evaluation method for upper limb motor function in stroke patients. Methods. Thirty-four stroke survivors and twenty-five age-matched healthy volunteers as the control group were recruited for this study. Inertial sensor data and surface electromyography (sEMG) signals were collected from the upper limb during voluntary upward reaching. Five features included max shoulder joint angle, peak and average speeds, torso balance calculated from inertial sensor data, and muscle synergy similarity extracted from sEMG data by the nonnegative matrix factorization algorithm. Meanwhile, the Fugl–Meyer score of each patient was graded by professional rehabilitation therapist. Results. Statistically significant differences were observed among severe, mild-to-moderate, and control group of five features ( p ≤ 0.001). The features varied as the level of upper limb motor function changes since these features significantly correlated with the Fugl–Meyer assessment scale ( p ≤ 0.001). Moreover, the Bland–Altman method was conducted and showed high consistency between the evaluation method of five features and Fugl–Meyer scale. Therefore, the five features proposed in this paper can quantitatively evaluate the motor function of stroke patients which is very useful in the rehabilitation process.

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Section: Muscle Synergy Analysissupporting

confidence: 81%

Motor Function Assessment of Upper Limb in Stroke Patients

Pan

Huang

Jin

et al. 2021

Journal of Healthcare Engineering

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“…First, we used the fourth-order Butterworth high-pass filter (cutoff frequency of 40 Hz) to filter the signal and then removed the means and rectification ( Myers et al, 2003 ). Then, we used the fourth-order Butterworth low-pass filter (cutoff frequency of 4 Hz) to extract the signal envelope and introduced the maximum normalization method ( Esmaeili and Maleki, 2019 ; Korak et al, 2020 ) to normalize each channel EMG data for every trial according to the maximum amplitude value in each channel. We preprocessed all signals for each movement according to the above methods and built a data matrix of L × K ( L was the number of muscles, K was the number of data points), which was set as the muscle activation matrix M .…”

Section: Methodsmentioning

confidence: 99%

“…The present studies mainly depended on the theories of dimension reduction and blind source separation, such as independent component analysis (ICA), principle component analysis (PCA), second-order blind identification (SOBI), and non-negative matrix factorization (NMF). Here, with respect to both ICA and PCA, two types of blind source separation can reveal several patterns of muscle synergy but have limitations in the specific assumptions in the extracted muscle synergy (orthogonality for PCA and statistical independence for ICA) and the quite highly mean communality of the data ( Ivanenko et al, 2004 ; Weiss and Flanders, 2004 ; Esmaeili and Maleki, 2019 ). Later, some studies tried to use the SOBI method for muscle synergy estimation, but it is the best algorithm with four channels (no dimension reduction) and is not suitable for this study ( Belouchrani et al, 1997 ; Ebied et al, 2018 ).…”

Section: Introductionmentioning

confidence: 99%

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

Chen

Dong

Feng

et al. 2023

Front. Hum. Neurosci.

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A core issue in motor control is how the central nervous system generates and selects the muscle activation patterns necessary to achieve a variety of behaviors and movements. Extensive studies have verified that it is the foundation to induce a complex movement by the modular combinations of several muscles with a synergetic relationship. However, a few studies focus on the synergetic similarity and dissimilarity among different types of movements, especially for the upper extremity movements. In this study, we introduced the non-negative matrix factorization (NMF) method to explore the muscle activation patterns and synergy structure under 6 types of movements, involving the hand open (HO), hand close (HC), wrist flexion (WF), wrist extension (WE), supination (SU), and pronation (PR). For this, we enrolled 10 healthy subjects to record the electromyography signal for NMF calculation. The results showed a highly modular similarity of the muscle synergy among subjects under the same movement. Furthermore, Spearman’s correlation analysis indicated significant similarities among HO-WE, HO-SU, and WE-SU (p < 0.001). Additionally, we also found shared synergy and special synergy in activation patterns among different movements. This study confirmed the theory of modular structure in the central nervous system, which yields a stable synergetic pattern under the same movement. Our findings on muscle synergy will be of great significance to motor control and even to clinical assessment techniques.

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“…This type of learning is transmitted to muscles by CNS with defined patterns to complete a motor skill (Shumway-Cook et al, 2007). These patterns, which a group of muscles uses, are recognized as muscle synergies (Abd et al, 2021;Esmaeili et al, 2019). Using involved muscle synergies is one of the current methods to investigate the relationship between the nerve system and the performance of athletes (Nowshiravan Rahatabad et al, 2021).…”

Section: The Proposed Approach To Compare the Performance Of Athletesmentioning

confidence: 99%

New Method to Assess and Enhance Athletes’ Performance Based on Muscle Synergy Patterns: A New Approach to Design a Biofeedback Training Scheme

Torbati

Gholibeigi²,

Jami³

2022

EJSPORT

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Objectives: There has been a big challenge for athletes at any level to reach their ultimate performance. According to a Motor Control theory, accurate programming of movements during the last stage of preparation to work out can improve athletes' performance. So, this study aims to design a performance analysis method that can be used to classify athletes and give them feedback during exercise. In this way, athletes and coaches can consider exercise schemes to modify and ameliorate athletes' performance. Methods: The participants were made up of 20 men between 25 to 30 years old. These subjects include ten professional football players and ten amateur football players. The same trials in 6 successive days were done by all athletes, both professional and amateur. Two different types of conditions were designed for all participants. In the first three days, subjects were asked to shoot a penalty kick (football player) 10 times in an actual situation like what they do in a competition. These procedures were done in the simulation condition, in the next three days designed by the computer (Xbox). Surface electromyography (sEMG) was recorded from the gastrocnemius and tibialis anterior muscles during trials. Results: Results showed the maximum error between basis vectors in all of the professional subjects in each situation, real and game, were 0.48 and 0.37, respectively (Mean square error index), which are very low relative to the range of data (5.1). In contrast, the minimum error among amateurs in each situation was 13.18 and 18.72, which are a high amount compared with the range of data (7). Discussion: Professional athletes always, in both situations, use their muscles in the same way. In fact, professional athletes' muscles follow specific patterns due to slight errors, while amateurs' muscles do not.

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Comparison of muscle synergies extracted from both legs during cycling at different mechanical conditions

Cited by 13 publications

References 47 publications

Motor Function Assessment of Upper Limb in Stroke Patients

Motor Function Assessment of Upper Limb in Stroke Patients

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

New Method to Assess and Enhance Athletes’ Performance Based on Muscle Synergy Patterns: A New Approach to Design a Biofeedback Training Scheme

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