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School of Medicine


This study reports a new technique for extracting muscle synergies using continuous wavelet transform. The method allows to quantify coincident activation of muscle groups caused by the physiological processes of fixed duration, thus enabling the extraction of wavelet modules of arbitrary groups of muscles. Hierarchical clustering and identification of the repeating wavelet modules across subjects and across movements, was used to identify consistent muscle synergies. Results indicate that the most frequently repeated wavelet modules comprised combinations of two muscles that are not traditional agonists and span different joints. We have also found that these wavelet modules were flexibly combined across different movement directions in a pattern resembling directional tuning. This method is extendable to multiple frequency domains and signal modalities.

Source Citation

Popov A, Olesh EV, Yakovenko S, Gritsenko V. A novel method of identifying motor primitives using wavelet decomposition. In: 2018 IEEE 15th International Conference on Wearable and Implantable Body Sensor Networks (BSN). IEEE; 2018. doi:10.1109/bsn.2018.8329674