Okajima Shotaro, Costa-García ÁLvaro, Ueda Sayako, Yang Ningjia, Shimoda Shingo
CBS- TOYOTA Collaboration Center, Center of Brain Science, RIKEN, 2271-130 Shimo-Shidami, Moriyama-ku, Nagoya, 463-0003, Japan.
Anat Rec (Hoboken). 2023 Apr;306(4):741-763. doi: 10.1002/ar.24910. Epub 2022 Apr 6.
Estimation of muscle activity using surface electromyography (sEMG) is an important non-invasive method that can lead to a deeper understanding of motor-control strategies in humans. Measurement using multiple active electrodes is necessary to estimate not only surface muscle activity but also deep muscle activity in dynamic motion. In this paper, we propose a method for estimating muscle activity of dynamic motions based on anatomical knowledge of muscle structures. To estimate muscle activity, a large number of signal sources are set in the muscle model, and connections between the signal sources are defined a priori based on the anatomical structure of the muscles. The signal source activities are first estimated by minimizing the Kullback-Leibler divergence with a continuity cost. Then, the muscle activity is computed from the signal source activity. In the experiments, five healthy participants performed five types of motion and the forearm sEMG was measured with 20-channel active electrodes. The estimation results for these motions were visualized in four dimensions as the three-dimensional position of the muscle over time. The results showed that the estimation was accurate, with a reproduction rate of 95% for the measured sEMG and continuity of the muscle activity. In addition, the results suggest the advantage of the proposed method over the conventional approaches in terms of estimating the muscle activity for both dynamic and abnormal motions.
使用表面肌电图（sEMG）估计肌肉活动是一种重要的非侵入性方法，有助于更深入地理解人类的运动控制策略。在动态运动中，不仅要估计表面肌肉活动，还要估计深层肌肉活动，因此需要使用多个有源电极进行测量。在本文中，我们提出了一种基于肌肉结构解剖学知识来估计动态运动肌肉活动的方法。为了估计肌肉活动，在肌肉模型中设置了大量信号源，并根据肌肉的解剖结构预先定义信号源之间的连接。首先通过最小化具有连续性代价的库尔贝克-莱布勒散度来估计信号源活动。然后，根据信号源活动计算肌肉活动。在实验中，五名健康参与者进行了五种类型的运动，并用20通道有源电极测量了前臂sEMG。这些运动的估计结果以肌肉随时间的三维位置在四个维度上进行可视化。结果表明，估计是准确的，测量的sEMG再现率为95%，肌肉活动具有连续性。此外，结果表明，在估计动态和异常运动的肌肉活动方面，该方法比传统方法具有优势。
