Robot-assisted rehabilitation offers an effective approach, wherein exoskeletons adapt to users' needs and provide personalized assistance. However, to deliver such assistance, accurate prediction of the user's joint torques is essential. In this work, we propose a feature extraction pipeline using 8-channel surface electromyography (sEMG) signals to predict elbow and shoulder joint torques. For preliminary evaluation, this pipeline was integrated into two neural network models: the Multilayer Perceptron (MLP) and the Temporal Convolutional Network (TCN). Data were collected from a single subject performing elbow and shoulder movements under three load conditions (0 kg, 1.10 kg, and 1.85 kg) using three motion-capture cameras. Reference torques were estimated from center-of-mass kinematics under the assumption of static equilibrium. Our offline analyses showed that, with our feature extraction pipeline, MLP model achieved mean RMSE of 0.963 N m, 1.403 N m, and 1.434 N m (over five seeds) for elbow, front-shoulder, and side-shoulder joints, respectively, which were comparable to the TCN performance. These results demonstrate that the proposed feature extraction pipeline enables a simple MLP to achieve performance comparable to that of a network designed explicitly for temporal dependencies. This finding is particularly relevant for applications with limited training data, a common scenario patient care.

翻译：机器人辅助康复提供了一种有效方法，其中外骨骼适应用户需求并提供个性化辅助。然而，为实现此类辅助，准确预测用户的关节力矩至关重要。本研究提出一种利用8通道表面肌电信号预测肘关节和肩关节力矩的特征提取流程。为进行初步评估，该流程被集成到两种神经网络模型中：多层感知器和时序卷积网络。数据采集自单名受试者使用三台运动捕捉相机在三种负载条件下执行肘部和肩部运动。参考力矩基于静态平衡假设从质心运动学数据估算得出。离线分析表明，采用本特征提取流程后，MLP模型在肘关节、前肩关节和侧肩关节上分别实现了0.963牛·米、1.403牛·米和1.434牛·米的平均均方根误差，其时序性能与TCN相当。这些结果证明，所提出的特征提取流程能使简单的MLP达到与专门为时序依赖设计的网络相媲美的性能。这一发现在训练数据有限的应用场景中尤为重要，而这正是患者护理中的常见情况。