{"title":"基于肌肉协同图神经网络的人体运动神经调节分析。","authors":"Ningjia Yang;Xuesi Li;Qi An;Jingsong Li;Shingo Shimoda","doi":"10.1109/TNSRE.2025.3557777","DOIUrl":null,"url":null,"abstract":"The coordination of muscles in human locomotion is commonly understood as the integration of motor modules known as muscle synergies. Recent research has delved into the adaptation of muscle synergies during the acquisition of new motor skills. However, the precise interplay between modulated muscle synergies during movement according to motion requirements remains unclear. Here, we aim to elucidate the alterations in locomotor synergies across various lower-limb motion strategies and motor tasks. Our findings reveal consistent weights of muscles in muscle synergies alongside varying timing activation aligned with specific motion requirements. It shows that spatial muscle synergies remain stable across different motor tasks, but humans adjusted the timing activation of these modules (temporal muscle synergies) to meet the motor requirements. To classify temporal muscle synergies and quantify connection weights for both self-connections and connections between muscle synergies, we employed a graph neural network. Our results demonstrate that muscle synergy 4, responsible for elevating the thigh to propel forward during the swing phase, experiences pronounced enhancement with changes in motion strategies. Furthermore, we observed a reduction in the self-connection of muscle synergy 2, implicated in stabilizing body posture, during motion tasks other than normal walking. Additionally, the connections between muscle synergy 2 and other synergies diminished, indicating more adaptation in muscle synergy 2 to achieve stabilization in more challenging motor tasks. The validity of these findings was verified through five-fold cross-validation, affirming the efficacy of our approach in elucidating neuro-modulation mechanisms in human locomotion. Our proposed methodology holds promising implications for the development of personalized training strategies, offering insights into the intricate interactions among different muscle synergies in accomplishing motor tasks.","PeriodicalId":13419,"journal":{"name":"IEEE Transactions on Neural Systems and Rehabilitation Engineering","volume":"33 ","pages":"1381-1391"},"PeriodicalIF":4.8000,"publicationDate":"2025-04-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10949291","citationCount":"0","resultStr":"{\"title\":\"Neuro-Modulation Analysis Based on Muscle Synergy Graph Neural Network in Human Locomotion\",\"authors\":\"Ningjia Yang;Xuesi Li;Qi An;Jingsong Li;Shingo Shimoda\",\"doi\":\"10.1109/TNSRE.2025.3557777\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The coordination of muscles in human locomotion is commonly understood as the integration of motor modules known as muscle synergies. Recent research has delved into the adaptation of muscle synergies during the acquisition of new motor skills. However, the precise interplay between modulated muscle synergies during movement according to motion requirements remains unclear. Here, we aim to elucidate the alterations in locomotor synergies across various lower-limb motion strategies and motor tasks. Our findings reveal consistent weights of muscles in muscle synergies alongside varying timing activation aligned with specific motion requirements. It shows that spatial muscle synergies remain stable across different motor tasks, but humans adjusted the timing activation of these modules (temporal muscle synergies) to meet the motor requirements. To classify temporal muscle synergies and quantify connection weights for both self-connections and connections between muscle synergies, we employed a graph neural network. Our results demonstrate that muscle synergy 4, responsible for elevating the thigh to propel forward during the swing phase, experiences pronounced enhancement with changes in motion strategies. Furthermore, we observed a reduction in the self-connection of muscle synergy 2, implicated in stabilizing body posture, during motion tasks other than normal walking. Additionally, the connections between muscle synergy 2 and other synergies diminished, indicating more adaptation in muscle synergy 2 to achieve stabilization in more challenging motor tasks. The validity of these findings was verified through five-fold cross-validation, affirming the efficacy of our approach in elucidating neuro-modulation mechanisms in human locomotion. Our proposed methodology holds promising implications for the development of personalized training strategies, offering insights into the intricate interactions among different muscle synergies in accomplishing motor tasks.\",\"PeriodicalId\":13419,\"journal\":{\"name\":\"IEEE Transactions on Neural Systems and Rehabilitation Engineering\",\"volume\":\"33 \",\"pages\":\"1381-1391\"},\"PeriodicalIF\":4.8000,\"publicationDate\":\"2025-04-04\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10949291\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"IEEE Transactions on Neural Systems and Rehabilitation Engineering\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://ieeexplore.ieee.org/document/10949291/\",\"RegionNum\":2,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"ENGINEERING, BIOMEDICAL\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Transactions on Neural Systems and Rehabilitation Engineering","FirstCategoryId":"5","ListUrlMain":"https://ieeexplore.ieee.org/document/10949291/","RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ENGINEERING, BIOMEDICAL","Score":null,"Total":0}
Neuro-Modulation Analysis Based on Muscle Synergy Graph Neural Network in Human Locomotion
The coordination of muscles in human locomotion is commonly understood as the integration of motor modules known as muscle synergies. Recent research has delved into the adaptation of muscle synergies during the acquisition of new motor skills. However, the precise interplay between modulated muscle synergies during movement according to motion requirements remains unclear. Here, we aim to elucidate the alterations in locomotor synergies across various lower-limb motion strategies and motor tasks. Our findings reveal consistent weights of muscles in muscle synergies alongside varying timing activation aligned with specific motion requirements. It shows that spatial muscle synergies remain stable across different motor tasks, but humans adjusted the timing activation of these modules (temporal muscle synergies) to meet the motor requirements. To classify temporal muscle synergies and quantify connection weights for both self-connections and connections between muscle synergies, we employed a graph neural network. Our results demonstrate that muscle synergy 4, responsible for elevating the thigh to propel forward during the swing phase, experiences pronounced enhancement with changes in motion strategies. Furthermore, we observed a reduction in the self-connection of muscle synergy 2, implicated in stabilizing body posture, during motion tasks other than normal walking. Additionally, the connections between muscle synergy 2 and other synergies diminished, indicating more adaptation in muscle synergy 2 to achieve stabilization in more challenging motor tasks. The validity of these findings was verified through five-fold cross-validation, affirming the efficacy of our approach in elucidating neuro-modulation mechanisms in human locomotion. Our proposed methodology holds promising implications for the development of personalized training strategies, offering insights into the intricate interactions among different muscle synergies in accomplishing motor tasks.
期刊介绍:
Rehabilitative and neural aspects of biomedical engineering, including functional electrical stimulation, acoustic dynamics, human performance measurement and analysis, nerve stimulation, electromyography, motor control and stimulation; and hardware and software applications for rehabilitation engineering and assistive devices.