A machine learning model for regression of interrupted Surface Electromyography (sEMG) signals to future control-oriented signals (e.g., robot’s joint angle and assistive torque) of an active biomechatronic device for high-level myoelectric-based hierarchical control is proposed. A Recurrent Neural Network (RNN) was trained using output data, initially obtained from offline optimization of the biomechatronic (human–robot) device and shifted by the prediction horizon. The input of the RNN consisted of interrupted sEMG signals (to mimic signal disconnections) and previous kinematic signals of the assistive system. The RNN with a 0.1-s prediction horizon could predict the control-oriented joint angle and assistive torque with 92% and 86.5% regression accuracy, respectively, for the test dataset. This proposed approach permits a fast, predictive, and direct estimation of control-oriented signals instead of an iterative process that optimizes assistive torque in the inverse dynamic simulation of a multibody human–robot system. Training with these interrupted input signals significantly improves the regression accuracy in the case of sEMG signal disconnection. This Robust Predictive Control-oriented Machine Learning (Robust-MuscleNET) model can support volitional high-level myoelectric-based control of biomechatronic devices, such as exoskeletons, prostheses, and assistive/resistive robots. Future work should study the application to prosthesis control as well as the repeatability of the high-level controller with electrode shift. The low-level hierarchical controller that manages the human–robot interaction, the assistance/resistance strategy, and the actuator coordination should also be studied.

提出了一种机器学习模型，用于将中断的表面肌电（sEMG）信号回归到主动生物机电设备的未来面向控制的信号（例如机器人的关节角度和辅助扭矩），以实现基于肌电的高级分层控制。使用输出数据训练循环神经网络（RNN），这些数据最初是从生物机电（人类-机器人）设备的离线优化中获得的，并根据预测范围进行移动。 RNN 的输入由中断的 sEMG 信号（模拟信号断开）和辅助系统之前的运动学信号组成。对于测试数据集，具有 0.1 秒预测范围的 RNN 可以分别以 92% 和 86.5% 的回归精度预测面向控制的关节角度和辅助扭矩。这种提出的方​​法允许快速、预测和直接估计控制导向信号，而不是在多体人体机器人系统的逆动态模拟中优化辅助扭矩的迭代过程。使用这些中断的输入信号进行训练可显着提高 sEMG 信号断开情况下的回归精度。这种面向鲁棒预测控制的机器学习 (Robust-MuscleNET) 模型可以支持生物机电设备（例如外骨骼、假肢和辅助/阻力机器人）的基于意志的高级肌电控制。未来的工作应该研究假肢控制的应用以及具有电极移位的高级控制器的可重复性。还应该研究管理人机交互、辅助/阻力策略和执行器协调的低级分层控制器。