College of Mechanical Engineering, Donghua University, Shanghai 201620, China.
School of Art, Design and Media, East China University of Science and Technology, Shanghai 200237, China.
Sensors (Basel). 2018 Sep 30;18(10):3296. doi: 10.3390/s18103296.
How to reconstruct drawing and handwriting traces from surface electromyography (sEMG) signals accurately has attracted a number of researchers recently. An effective algorithm is crucial to reliable reconstruction. Previously, nonlinear regression methods have been utilized successfully to some extent. In the quest to improve the accuracy of transient myoelectric signal decoding, a novel hybrid algorithm KF-GEP fusing Gene Expression Programming (GEP) into Kalman Filter (KF) framework is proposed for sEMG-based drawing trace reconstruction. In this work, the KF-GEP was applied to reconstruct fourteen drawn shapes and ten numeric characters from sEMG signals across five participants. Then the reconstruction performance of KF-GEP, KF and GEP were compared. The experimental results show that the KF-GEP algorithm performs best because it combines the advantages of KF and GEP. The findings add to the literature on the muscle-computer interface and can be introduced to many practical fields.
如何从表面肌电信号（sEMG）准确地重建绘画和手写轨迹，最近引起了许多研究人员的关注。一个有效的算法对于可靠的重建至关重要。以前，非线性回归方法已经在一定程度上得到了成功的应用。为了提高瞬态肌电信号解码的准确性，提出了一种新的混合算法 KF-GEP，该算法将基因表达式编程（GEP）融合到卡尔曼滤波器（KF）框架中，用于基于 sEMG 的绘画轨迹重建。在这项工作中，KF-GEP 被应用于从五个参与者的 sEMG 信号中重建十四个绘制形状和十个数字字符。然后比较了 KF-GEP、KF 和 GEP 的重建性能。实验结果表明，KF-GEP 算法表现最好，因为它结合了 KF 和 GEP 的优点。该研究结果丰富了肌电接口领域的文献，并可应用于许多实际领域。
