A Novel Approach to the Segmentation of sEMG Data Based on the Activation and Deactivation of Muscle Synergies During Movement

Costa‐García, Álvaro; Itkonen, Matti; Yamasaki, Hiroshi; Alnajjar, Fady; Shimoda, Shingo

doi:10.1109/lra.2018.2811506

Cited by 12 publications

(4 citation statements)

References 30 publications

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“…The key here is to select the appropriate number of synergies s to extract from the sEMG signals. In general, E decreases with increasing number of synergies s, but some of the extracted syner-gies become unstable if s is larger than the appropriate value [18,19]. In our previous study [13], we showed that muscle synergies during swallowing could be extracted stably when s is set at 2 or 3.…”

Section: Individual Analysis Of Semg Signals Divided Into Two Analysi...mentioning

confidence: 88%

Method for Evaluating Muscle Fatigue from Multi-channel Surface Electromyography Signals of Suprahyoid and Infrahyoid Muscles During Swallowing

YOKOHAMA,

SASAKI,

KAMATA

et al. 2024

ABE

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Fatigue of the muscles involved in swallowing is regarded as one of the factors that increase the risk of aspiration and choking. However, an objective method for assessing muscle fatigue in swallowing-related muscles during the act of swallowing, which consists of voluntary and involuntary reflexive movements and lasts about 1 s, has yet to be established. Therefore, we aimed to develop a method to noninvasively evaluate muscle fatigue from surface electromyography (sEMG) signals recorded during swallowing. In 12 younger and 11 older healthy adults, 44-channel sEMG signals were measured during swallowing before and after a fatiguing task (FT). The FT involved isometric tongue pressure generation to maintain a load of 60% of the maximum pressure. Muscle synergy analysis was used as an event detector, and the 44-channel sEMG signals were divided into two analysis ranges: one containing muscle activity mainly during the oral phase (Pre-indexA) and the other containing muscle activity mainly during the swallow reflex (Post-indexA). The sEMG signals of each range were converted to swallowing pattern images, and the similarity of the images before and after FT was calculated as the Euclidean distance (ED), using a convolutional neural network and kernel principal component analysis. The results revealed that the normalized ED in Pre-indexA differed significantly between the younger and older adults. This finding suggests the possibility of quantitatively evaluating differences in age-related muscle fatigue from sEMG signals recorded during swallowing.

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Section: Individual Analysis Of Semg Signals Divided Into Two Analysi...mentioning

confidence: 88%

Method for Evaluating Muscle Fatigue from Multi-channel Surface Electromyography Signals of Suprahyoid and Infrahyoid Muscles During Swallowing

YOKOHAMA,

SASAKI,

KAMATA

et al. 2024

ABE

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“…In our protocol, we tried to avoid/reduce muscle fatigue; although this might not be fully possible, especially in the case of more demanding scenarios (e.g., during repetitive training tasks for poststroke treatment), it should be noted that muscle fatigue reduces strength and increases perceived effort, as observed in joint kinematics and movement complexity analyses in healthy individuals [54]. However, these changes due to muscle fatigue do not reflect alterations in the overall principal component shape [55, 56].…”

Section: Discussionmentioning

confidence: 99%

Motor Control System for Adaptation of Healthy Individuals and Recovery of Poststroke Patients: A Case Study on Muscle Synergies

et al. 2019

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Understanding the complex neuromuscular strategies underlying behavioral adaptation in healthy individuals and motor recovery after brain damage is essential for gaining fundamental knowledge on the motor control system. Relying on the concept of muscle synergy, which indicates the number of coordinated muscles needed to accomplish specific movements, we investigated behavioral adaptation in nine healthy participants who were introduced to a familiar environment and unfamiliar environment. We then compared the resulting computed muscle synergies with those observed in 10 moderate-stroke survivors throughout an 11-week motor recovery period. Our results revealed that computed muscle synergy characteristics changed after healthy participants were introduced to the unfamiliar environment, compared with those initially observed in the familiar environment, and exhibited an increased neural response to unpredictable inputs. The altered neural activities dramatically adjusted through behavior training to suit the unfamiliar environment requirements. Interestingly, we observed similar neuromuscular behaviors in patients with moderate stroke during the follow-up period of their motor recovery. This similarity suggests that the underlying neuromuscular strategies for adapting to an unfamiliar environment are comparable to those used for the recovery of motor function after stroke. Both mechanisms can be considered as a recall of neural pathways derived from preexisting muscle synergies, already encoded by the brain’s internal model. Our results provide further insight on the fundamental principles of motor control and thus can guide the future development of poststroke therapies.

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“…According to this model, during motor learning, the plastic nature of neurons creates modules or neural networks (called synergies) that are specialized for different tasks [10, 11]. Synergies control the contraction of a set of muscles using a low-dimensional set of control commands originating from the brain [12–15]. It has been proposed that use of the remaining neural pathways could potentiate post-stroke recovery.…”

Section: Introductionmentioning

confidence: 99%

Model for prompt and effective classification of motion recovery after stroke considering muscle strength and coordination factors

Costa‐García¹,

Yamasaki²,

Itkonen³

et al. 2019

J NeuroEngineering Rehabil

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BackgroundMuscle synergies are now widely discussed as a method for evaluating the existence of redundant neural networks that can be activated to enhance stroke rehabilitation. However, this approach was initially conceived to study muscle coordination during learned motions in healthy individuals. After brain damage, there are several neural adaptations that contribute to the recovery of motor strength, with muscle coordination being one of them. In this study, a model is proposed that assesses motion based on surface electromyography (sEMG) according to two main factors closely related to the neural adaptations underlying motor recovery: (1) the correct coordination of the muscles involved in a particular motion and (2) the ability to tune the effective strength of each muscle through muscle fiber contractions. These two factors are hypothesized to be affected differently by brain damage. Therefore, their independent evaluation will play an important role in understanding the origin of stroke-related motor impairments.ResultsThe model proposed was validated by analyzing sEMG data from 18 stroke patients with different paralysis levels and 30 healthy subjects. While the factors necessary to describe motion were stable across heathy subjects, there was an increasing disassociation for stroke patients with severe motor impairment.ConclusionsThe clear dissociation between the coordination of muscles and the tuning of their strength demonstrates the importance of evaluating these factors in order to choose appropriate rehabilitation therapies. The model described in this research provides an efficient approach to promptly evaluate these factors through the use of two intuitive indexes.

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A Novel Approach to the Segmentation of sEMG Data Based on the Activation and Deactivation of Muscle Synergies During Movement

Cited by 12 publications

References 30 publications

Method for Evaluating Muscle Fatigue from Multi-channel Surface Electromyography Signals of Suprahyoid and Infrahyoid Muscles During Swallowing

Method for Evaluating Muscle Fatigue from Multi-channel Surface Electromyography Signals of Suprahyoid and Infrahyoid Muscles During Swallowing

Motor Control System for Adaptation of Healthy Individuals and Recovery of Poststroke Patients: A Case Study on Muscle Synergies

Model for prompt and effective classification of motion recovery after stroke considering muscle strength and coordination factors

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