Lobov Sergey, Krilova Nadia, Kastalskiy Innokentiy, Kazantsev Victor, Makarov Valeri A
Lobachevsky State University of Nizhny Novgorod, Gagarin Ave. 23, 603950 Nizhny Novgorod, Russia.
Department of Applied Mathematics, Instituto de Matemática Interdisciplinar, Universidad Complutense de Madrid, 28040 Madrid, Spain.
Sensors (Basel). 2018 Apr 6;18(4):1122. doi: 10.3390/s18041122.
Recent advances in recording and real-time analysis of surface electromyographic signals (sEMG) have fostered the use of sEMG human-machine interfaces for controlling personal computers, prostheses of upper limbs, and exoskeletons among others. Despite a relatively high mean performance, sEMG-interfaces still exhibit strong variance in the fidelity of gesture recognition among different users. Here, we systematically study the latent factors determining the performance of sEMG-interfaces in synthetic tests and in an arcade game. We show that the degree of muscle cooperation and the amount of the body fatty tissue are the decisive factors in synthetic tests. Our data suggest that these factors can only be adjusted by long-term training, which promotes fine-tuning of low-level neural circuits driving the muscles. Short-term training has no effect on synthetic tests, but significantly increases the game scoring. This implies that it works at a higher decision-making level, not relevant for synthetic gestures. We propose a procedure that enables quantification of the gestures' fidelity in a dynamic gaming environment. For each individual subject, the approach allows identifying "problematic" gestures that decrease gaming performance. This information can be used for optimizing the training strategy and for adapting the signal processing algorithms to individual users, which could be a way for a qualitative leap in the development of future sEMG-interfaces.
近年来，表面肌电信号（sEMG）记录与实时分析技术的进步推动了sEMG人机接口在控制个人电脑、上肢假肢和外骨骼等方面的应用。尽管平均性能相对较高，但sEMG接口在不同用户之间的手势识别保真度方面仍表现出很大差异。在此，我们系统地研究了在合成测试和街机游戏中决定sEMG接口性能的潜在因素。我们表明，肌肉协作程度和身体脂肪组织量是合成测试中的决定性因素。我们的数据表明，这些因素只能通过长期训练来调节，长期训练可促进驱动肌肉的低级神经回路的微调。短期训练对合成测试没有影响，但能显著提高游戏得分。这意味着它在更高的决策层面起作用，与合成手势无关。我们提出了一种程序，能够在动态游戏环境中量化手势的保真度。对于每个个体受试者，该方法允许识别降低游戏性能的“有问题”手势。这些信息可用于优化训练策略以及使信号处理算法适应个体用户，这可能是未来sEMG接口发展实现质的飞跃的一种途径。
