Deep-learning models can realize the feature extraction and advanced abstraction of raw myoelectric signals without necessitating manual selection. Raw surface myoelectric signals are processed with a deep model in this study to investigate the feasibility of recognizing upper-limb motion intents and real-time control of auxiliary equipment for upper-limb rehabilitation training. Surface myoelectric signals are collected on six motions of eight subjects’ upper limbs. A light-weight convolutional neural network (Lw-CNN) and support vector machine (SVM) model are designed for myoelectric signal pattern recognition. The offline and online performance of the two models are then compared. The average accuracy is (90 ± 5)% for the Lw-CNN and (82.5 ± 3.5)% for the SVM in offline testing of all subjects, which prevails over (84 ± 6)% for the online Lw-CNN and (79 ± 4)% for SVM. The robotic arm control accuracy is (88.5 ± 5.5)%. Significance analysis shows no significant correlation ( = 0.056) among real-time control, offline testing, and online testing. The Lw-CNN model performs well in the recognition of upper-limb motion intents and can realize real-time control of a commercial robotic arm.

中文翻译：

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基于Lw-CNN的上肢康复机器人手臂肌电信号识别和实时控制

深度学习模型可以实现原始肌电信号的特征提取和高级抽象，而无需手动选择。在本研究中，使用深层模型处理原始表面肌电信号，以研究识别上肢运动意图和实时控制辅助设备进行上肢康复训练的可行性。在八个对象的上肢的六个动作中收集表面肌电信号。轻型卷积神经网络（Lw-CNN）和支持向量机（SVM）模型被设计用于肌电信号模式识别。然后比较两种模型的离线和在线性能。在所有受试者的离线测试中，Lw-CNN的平均准确度为（90±5）％，SVM的平均准确度为（82.5±3.5）％，在线Lw-CNN占（84±6）％，SVM占（79±4）％。机械臂控制精度为（88.5±5.5）％。显着性分析显示无显着相关性（ = 0.056），包括实时控制，离线测试和在线测试。Lw-CNN模型在识别上肢运动意图方面表现良好，并且可以实现商用机械臂的实时控制。