通过肌电优化个人化肌肉模型，精准估算训练中的肌肉激活
Muscle Activation Estimation by Optimizing the Musculoskeletal Model for Personalized Strength and Conditioning Training
- 用肌电数据优化全身肌肉骨骼模型参数
- 在卧推和硬拉测试中验证了肌肉激活估计的准确性
- 适合运动科学与个性化训练研究者使用
肌肉骨骼模型在康复和抗阻训练领域至关重要，用于分析肌肉状态。然而，个体间肌肉骨骼参数差异以及部分内部生物力学变量无法直接测量，给精准个性化建模带来挑战。此外，由于肌肉系统固有的冗余性（多个肌肉协同驱动单一关节），肌肉激活估计尤为困难。本研究构建了适用于力量与体能训练的全身肌肉骨骼模型，并采用基于肌电图的优化方法校准相关肌肉参数。利用该个性化模型，可后续估算肌肉激活水平，进而分析动作表现。以卧推和硬拉为实验验证，证实了该方法的有效性。
原文摘要 · Abstract (English)
Musculoskeletal models are pivotal in the domains of rehabilitation and resistance training to analyze muscle conditions. However, individual variability in musculoskeletal parameters and the immeasurability of some internal biomechanical variables pose significant obstacles to accurate personalized modelling. Furthermore, muscle activation estimation can be challenging due to the inherent redundancy of the musculoskeletal system, where multiple muscles drive a single joint. This study develops a whole-body musculoskeletal model for strength and conditioning training and calibrates relevant muscle parameters with an electromyography-based optimization method. By utilizing the personalized musculoskeletal model, muscle activation can be subsequently estimated to analyze the performance of exercises. Bench press and deadlift are chosen for experimental verification to affirm the efficacy of this approach.
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