Mongardi Andrea, Rossi Fabio, Prestia Andrea, Motto Ros Paolo, Demarchi Danilo
Department of Electronics and Telecommunications, Politecnico di Torino, 10129 Turin, Italy.
Sensors (Basel). 2025 Mar 30;25(7):2188. doi: 10.3390/s25072188.
Hand gesture recognition is a prominent topic in the recent literature, with surface ElectroMyoGraphy (sEMG) recognized as a key method for wearable Human-Machine Interfaces (HMIs). However, sensor placement still significantly impacts systems performance. This study addresses sensor displacement by introducing a fast and low-impact orientation correction algorithm for sEMG-based HMI armbands. The algorithm includes a calibration phase to estimate armband orientation and real-time data correction, requiring only two distinct hand gestures in terms of sEMG activation. This ensures hardware and database independence and eliminates the need for model retraining, as data correction occurs prior to classification or prediction. The algorithm was implemented in a hand gesture HMI system featuring a custom seven-channel sEMG armband with an Artificial Neural Network (ANN) capable of recognizing nine gestures. Validation demonstrated its effectiveness, achieving 93.36% average prediction accuracy with arbitrary armband wearing orientation. The algorithm also has minimal impact on power consumption and latency, requiring just an additional 500 μW and introducing a latency increase of 408 μs. These results highlight the algorithm's efficacy, general applicability, and efficiency, presenting it as a promising solution to the electrode-shift issue in sEMG-based HMI applications.
手势识别是近期文献中的一个热门话题，表面肌电图（sEMG）被认为是可穿戴人机接口（HMI）的关键方法。然而，传感器的放置仍然会对系统性能产生显著影响。本研究通过为基于sEMG的HMI臂带引入一种快速且影响较小的方向校正算法，来解决传感器位移问题。该算法包括一个校准阶段，用于估计臂带方向和实时数据校正，在sEMG激活方面仅需要两种不同的手势。这确保了硬件和数据库的独立性，并且无需重新训练模型，因为数据校正是在分类或预测之前进行的。该算法在一个手势HMI系统中实现，该系统配备了一个定制的七通道sEMG臂带和一个能够识别九种手势的人工神经网络（ANN）。验证证明了其有效性，在臂带任意佩戴方向的情况下，平均预测准确率达到了93.36%。该算法对功耗和延迟的影响也最小，仅额外增加500 μW的功耗，延迟增加408 μs。这些结果凸显了该算法的有效性、普遍适用性和效率，使其成为基于sEMG的HMI应用中电极移位问题的一个有前景的解决方案。
