A neuromuscular electrical stimulation strategy based on muscle synergy for stroke rehabilitation

Cheng, Zihao; Márquez, Juan Carlos; Qu, Hongen; He, Xin; Lan, Ning

doi:10.1109/ner.2015.7146748

Cited by 11 publications

(8 citation statements)

References 13 publications

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“…As well as previous studies have demonstrated that the Central Nervous System (CNS) makes use of muscle synergies as a neural strategy to simplify the control of a variety of movements by using a single pattern of neural command signal [16]. So extracted synergy patterns of this study can be used in rehabilitation [15] and diagnosis [10]. Whether the source of pain in lumber is muscles or not can be determined easily by comparing muscles patterns of healthy subjects with patients who had pain in the lumber [10].…”

Section: Discussionmentioning

confidence: 77%

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Clinical Model to the Analysis of Synergy Pattern Changes of Back Muscles and its Relationship with the Occurrence of Fatigue

Torbati¹,

Tahami²,

Kobravi³

2018

TOBIOIJ

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Background:Right sitting not only leads to flatness of the lumbar spine and waistline, it also causes other problems for health. The curved body pushes into lungs and breathing will be problematic.Purpose:The main purpose of this study was investigating changing procedure of lumber muscles patterns and its relationship with the occurrence of fatigue.Methods:Participants were ten male with average age 24 ± 1. Firstly, the process of fatigue during sitting was observed by mid-frequency index. For performing the necessary analysis, the 10-second window of time was used. The 15 minutes of time trial was divided into 3 sub-terms. Each sub-term was investigated separately. The sub-terms contain: The beginning of record until the 90th second, from the 90th second to the 600th second and from the 600th second to the 900th second.Results:Results showed that in each subject there were synergy patterns in both of the first and the second sub-terms. Maximum error between basis vectors in all of the subjects were 0.87 and 0.79 respectively and standard deviations were 5 and 10 respectively (Mean square error index). In some participants, there were not any synergy patterns in the third sub-term (minimum error between basis vectors in all of the subjects was 18 and standard deviation was 7.5) while in other participants, their muscles still followed special synergy patterns (maximum error between basis vectors in all of the subjects was 0.98 standard deviation was 7.5).Conclusions:Comparing the synergy patterns between different participants has determined that the synergy patterns were the same only in the first sub-term.

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Section: Discussionmentioning

confidence: 77%

“…Synergy includes two types of synchronous (constant Synergy and variable coefficients) and Asynchronous (variable synergy and constant coefficients) [14]. Synergy pattern can be used for functional electrical stimulus [15]. Every muscle performance will be divided into linear vectors.…”

Section: Synergymentioning

confidence: 99%

Clinical Model to the Analysis of Synergy Pattern Changes of Back Muscles and its Relationship with the Occurrence of Fatigue

Torbati¹,

Tahami²,

Kobravi³

2018

TOBIOIJ

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show abstract

“…Previous studies have demonstrated that the central nervous system (CNS) makes use of muscle synergies as a neural strategy to simplify the control of a variety of movements by using a single pattern of neural command signal. So extracted synergy patterns of this study can be used in rehabilitation (C. Zhuang et al, 2015). That is to say, stimulation with specific patterns can be used instead of mentioned recovery because in some situation athletic cannot use these recovery, for example, in locker room between two half time of competition.…”

Section: Discussionmentioning

confidence: 99%

“…Synergy includes two types of synchronous (constant Synergy and variable coefficients) and Asynchronous (variable synergy and constant coefficients). Synergy pattern can be used for functional electrical stimulus ( Zhuang C et al, 2015). Each muscle performance will be divided into linear vectors, every moment, the sum of these vectors with specific coefficients Reconstruct electromyogram signal.…”

Section: Synergymentioning

confidence: 99%

Determination of the best recovery based on muscles synergy patterns and lactic acid

Torbati

Abbasnezhad

Tahami

2017

jhse

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The determination of the best recovery after an anaerobic exercise is an important challenge for professional athletes. This study compared and analyzed three methods that are often used in professional teams included: 1. Cold water pool 2. Use the massager 3. Running with 40 to 50 percent of heart rate. Methods: In this work recovery the 15 minutes recovery is done immediately after doing exercise. The i mpact of a particular method of recovery is quantified via lactic acid in the blood after the recovery and the synergy patterns of muscle activity. In each method, Biceps femoris, rectus femoris, tibialis anterior, lateral gastrocnemius muscles were analyzed. Results showed that there were synergy patterns in two running and ice methods, because maximum errors between basis vectors in all of the subjects were 0.13 and 0.18 respectively and Standard deviation of maximum MSE errors for all subjects is 6 (MSE index), whereas in massage recovery synergy has not been recognized because minimum error between basis vectors in all of the subjects was 4.29 and Standard deviation of maximum MSE error for all subjects is 4. Running has the best result in evacuating lactic acid. However, result in the ice method is similar to running.

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“…sEMG analysis has been investigated a lot nowadays. Recent experiments have suggested that the central nervous system (CNS) makes use of muscle synergies as a neural strategy to simplify the control of a variety of movements [7]. These synergies can compensate mechanical disabilities, resulting invisible to an external evaluation [8].…”

Section: Introductionmentioning

confidence: 99%

A Pilot Study of Individual Muscle Force Prediction during Elbow Flexion and Extension in the Neurorehabilitation Field

Hou

Sun

et al. 2016

Sensors

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This paper proposes a neuromusculoskeletal (NMS) model to predict individual muscle force during elbow flexion and extension. Four male subjects were asked to do voluntary elbow flexion and extension. An inertial sensor and surface electromyography (sEMG) sensors were attached to subject's forearm. Joint angle calculated by fusion of acceleration and angular rate using an extended Kalman filter (EKF) and muscle activations obtained from the sEMG signals were taken as the inputs of the proposed NMS model to determine individual muscle force. The result shows that our NMS model can predict individual muscle force accurately, with the ability to reflect subject-specific joint dynamics and neural control solutions. Our method incorporates sEMG and motion data, making it possible to get a deeper understanding of neurological, physiological, and anatomical characteristics of human dynamic movement. We demonstrate the potential of the proposed NMS model for evaluating the function of upper limb movements in the field of neurorehabilitation.

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A neuromuscular electrical stimulation strategy based on muscle synergy for stroke rehabilitation

Cited by 11 publications

References 13 publications

Clinical Model to the Analysis of Synergy Pattern Changes of Back Muscles and its Relationship with the Occurrence of Fatigue

Clinical Model to the Analysis of Synergy Pattern Changes of Back Muscles and its Relationship with the Occurrence of Fatigue

Determination of the best recovery based on muscles synergy patterns and lactic acid

A Pilot Study of Individual Muscle Force Prediction during Elbow Flexion and Extension in the Neurorehabilitation Field

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