College of Physical Education and Sports, Beijing Normal University, Beijing, China.
College of Physical Education and Sports, Beijing Normal University, Beijing, China.
Med Eng Phys. 2023 Oct;120:104054. doi: 10.1016/j.medengphy.2023.104054. Epub 2023 Sep 12.
Muscle synergy containing temporal and spatial patterns of muscle activity has been frequently used in prediction of kinematic characteristics. However, there is often some discrepancy between the predicted results based on muscle synergy and the actual movement performance. This study aims to propose a new method for compensating muscle synergy that allows the compensated synergy signal to predict kinematic characteristics more accurately. The study used the change of direction in running as background. Non-negative matrix factorisation was used to extract the muscle synergy during the change of direction at different angles. A non-linear association between synergy and the height of pelvic mass centre was established using long and short-term memory neural networks. Based on this model, the height fluctuations of the pelvic centre of mass are used as input and predict the fluctuations of the synergy which were used to compensate for the original synergy in different ways. The accuracy of the synergies compensated in different ways in predicting pelvic centre of mass movement was then assessed by back propagation neural networks. It was found that the compensated synergy significantly improves accuracy in predicting pelvic centre of mass displacement (R, p < 0.05). The predicted results of all-compensation are significantly different from actual performance in the end-swing (p < 0.05). The predicted results of half-compensation do not differ significantly from the actual performance, and its damage to the original synergy is smaller and does not increase with angle compared to all-compensation. The all-compensation may be affected by individual variability and lead to increased errors. The half-compensation can improve the predictive accuracy of the synergy while reducing the adjustment to the original synergy.
肌肉协同作用包含肌肉活动的时间和空间模式，已被广泛用于预测运动学特征。然而，基于肌肉协同作用预测的结果与实际运动表现之间常常存在一些差异。本研究旨在提出一种新的补偿肌肉协同作用的方法，使补偿后的协同信号能够更准确地预测运动学特征。本研究以跑步中的变向运动为背景。使用非负矩阵分解（Non-negative matrix factorization）从不同角度的变向运动中提取肌肉协同作用。使用长短时记忆神经网络（long and short-term memory neural networks）建立协同作用与骨盆质心高度之间的非线性关系。基于该模型，将骨盆质心高度的波动作为输入，预测协同作用的波动，然后以不同方式补偿原始协同作用。最后，使用反向传播神经网络（back propagation neural networks）评估以不同方式补偿的协同作用在预测骨盆质心运动中的准确性。结果发现，以不同方式补偿的协同作用在预测骨盆质心位移方面显著提高了准确性（R，p<0.05）。所有补偿的预测结果在结束摆动时与实际性能显著不同（p<0.05）。完全补偿的预测结果与实际性能没有显著差异，与完全补偿相比，其对原始协同作用的损伤较小，且不随角度增加而增加。完全补偿可能受到个体差异的影响，导致误差增加。半补偿可以在减少对原始协同作用的调整的同时提高协同作用的预测准确性。
