Abstract
Musculoskeletal models (MMs) driven by electromyography (EMG) signals have been used to predict human movements. Muscle excitations of MMs are generally the amplitude of EMG, which shows large variability even when repeating the same task. The general structure of muscle synergies has been proved to be consistent across test sessions, providing a perspective for extracting stable control information for MMs. Although non-negative matrix factorization (NMF) is a common method for extracting synergies, the factorization result of NMF is not unique. In this study, we proposed an improved NMF algorithm for extracting stable control information of MMs to predict hand and wrist motions. Specifically, we supplemented the Hadamard product and L2-norm regularization term to the objective function of NMF. The proposed NMF was utilized to identify stable muscle synergies. Then, the time-varying profile of each synergy was fed into a subject-specific MM for estimating joint motions. The results demonstrated that the proposed scheme significantly outperformed a traditional MM and an MM combined with the classic NMF (NMF-MM), with averaged R and NRMSE equal to \(0.89\pm 0.06\) and \(0.16\pm 0.04\). Further, the similarity between muscle synergies extracted from different training data revealed the proposed method’s effectiveness of identifying consistent control information for MMs. This study provides a novel model-based scheme for the estimation of continuous movements.
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Acknowledgements
The authors would like to thank all the participants in the experiments. This work was supported by the National Natural Science Foundation of China under Grant 91948302 and Grant 51905339.
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Zhao, J., Yu, Y., Sheng, X., Zhu, X. (2022). Extracting Stable Control Information from EMG Signals to Drive a Musculoskeletal Model - A Preliminary Study. In: Liu, H., et al. Intelligent Robotics and Applications. ICIRA 2022. Lecture Notes in Computer Science(), vol 13456. Springer, Cham. https://doi.org/10.1007/978-3-031-13822-5_66
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