Feng Luying, Yu Linfan, Lyu Hui, Yang Canjun, Liu Xiaoguang, Zhou Congcong, Yang Wei
Ningbo Innovation Center, Zhejiang University, Ningbo, China; College of Mechanical Engineering, Zhejiang University, Hangzhou, China.
Ningbo Innovation Center, Zhejiang University, Ningbo, China.
Hum Mov Sci. 2024 Dec;98:103300. doi: 10.1016/j.humov.2024.103300. Epub 2024 Nov 1.
Recent studies suggest that muscle synergy patterns can be a guide for diagnosis and rehabilitation.
Does human's lower limb synergy pattern significantly change with changes in walking speed? Are there large differences in synergy patterns among different healthy individuals?
22 healthy subjects from an open-source datasets were included. Non-negative matrix factorization was applied to identify the module composition of surface electromyography(sEMG) data, and the similarity index was adopted to quantify the overall similarity between synergy patterns.
Results demonstrated that healthy individuals have their own intrinsic muscle recruitment and coordination characteristics for locomotion at various speeds, additionally, their synergy patterns exhibit predictability under speed variations.
This study develop reference synergy patterns for the lower limbs across 28 different walking speeds. The developed synergy patterns and the above findings may guide the study of gait synergy in rehabilitation and assistance.
近期研究表明，肌肉协同模式可作为诊断和康复的指导。
人类下肢协同模式是否会随着步行速度的变化而显著改变？不同健康个体之间的协同模式是否存在较大差异？
纳入了来自开源数据集的22名健康受试者。应用非负矩阵分解来识别表面肌电图（sEMG）数据的模块组成，并采用相似性指数来量化协同模式之间的总体相似性。
结果表明，健康个体在不同速度下的运动具有其自身内在的肌肉募集和协调特征，此外，他们的协同模式在速度变化下具有可预测性。
本研究建立了28种不同步行速度下下肢的参考协同模式。所建立的协同模式及上述发现可能会指导康复和辅助中步态协同的研究。
