Piovanelli E, Piovesan D, Shirafuji S, Ota J
IEEE Int Conf Rehabil Robot. 2019 Jun;2019:405-410. doi: 10.1109/ICORR.2019.8779462.
In the recent years important steps forward have been made in the field of signal processing on muscle signals for hand prosthetics control. At the state of the art different algorithms and techniques allow a precise estimation of hand movements. However, they mostly work exclusively on the electrode space, not seeking for any information about the currents on the contracted muscles.In this study we propose a novel simplified method to estimate the muscles currents in the forearm, along with a first experimental application on two simple movements to assess its performance. We modeled the signal propagation from muscles to electrodes using a purely resistive electrical networks and afterwards apply the graph theory to assess the muscle currents. The proposed method considerably simplify the estimation of muscle's current, decreasing the problem complexity, and therefore potentially it can be a suitable approach for future prosthetics' control.
近年来，在用于手部假肢控制的肌肉信号处理领域取得了重要进展。在当前技术水平下，不同的算法和技术能够对手部运动进行精确估计。然而，它们大多仅在电极空间上起作用，并未寻求有关收缩肌肉上电流的任何信息。在本研究中，我们提出了一种新颖的简化方法来估计前臂中的肌肉电流，并首次在两个简单运动上进行实验应用以评估其性能。我们使用纯电阻性电气网络对从肌肉到电极的信号传播进行建模，然后应用图论来评估肌肉电流。所提出的方法极大地简化了肌肉电流的估计，降低了问题的复杂性，因此它有可能成为未来假肢控制的合适方法。
