Objective. This paper proposes machine learning models for mapping surface electromyography (sEMG) signals to regression of joint angle, joint velocity, joint acceleration, joint torque, and activation torque. Approach. The regression models, collectively known as MuscleNET, take one of four forms: ANN (forward artificial neural network), RNN (recurrent neural network), CNN (convolutional neural network), and RCNN (recurrent convolutional neural network). Inspired by conventional biomechanical muscle models, delayed kinematic signals were used along with sEMG signals as the machine learning model’s input; specifically, the CNN and RCNN were modeled with novel configurations for these input conditions. The models’ inputs contain either raw or filtered sEMG signals, which allowed evaluation of the filtering capabilities of the models. The models were trained using human experimental data and evaluated with different individual data. Main results. Results were compared in terms of regression error (using the root-mean-square) and model computation delay. The results indicate that the RNN (with filtered sEMG signals) and RCNN (with raw sEMG signals) models, both with delayed kinematic data, can extract underlying motor control information (such as joint activation torque or joint angle) from sEMG signals in pick-and-place tasks. The CNNs and RCNNs were able to filter raw sEMG signals. Significance. All forms of MuscleNET were found to map sEMG signals within 2 ms, fast enough for real-time applications such as the control of exoskeletons or active prostheses. The RNN model with filtered sEMG and delayed kinematic signals is particularly appropriate for applications in musculoskeletal simulation and biomechatronic device control.

客观的。本文提出了机器学习模型，用于将表面肌电图 (sEMG) 信号映射到关节角度、关节速度、关节加速度、关节扭矩和激活扭矩的回归。方法。回归模型统称为 MuscleNET，采用四种形式之一：ANN（前向人工神经网络）、RNN（循环神经网络）、CNN（卷积神经网络）和 RCNN（循环卷积神经网络）。受传统生物力学肌肉模型的启发，延迟运动信号与 sEMG 信号一起用作机器学习模型的输入；具体来说，CNN 和 RCNN 是针对这些输入条件用新颖的配置建模的。模型的输入包含原始或过滤后的 sEMG 信号，可用于评估模型的过滤能力。这些模型使用人类实验数据进行训练，并使用不同的个体数据进行评估。主要结果。结果在回归误差（使用均方根）和模型计算延迟方面进行了比较。结果表明，RNN（带有过滤后的 sEMG 信号）和 RCNN（带有原始 sEMG 信号）模型都具有延迟的运动学数据，可以从pick-并放置任务。CNN 和 RCNN 能够过滤原始 sEMG 信号。意义。发现所有形式的 MuscleNET 都可以在 2 ms 内绘制 sEMG 信号，对于实时应用（例如外骨骼或主动假肢的控制）来说足够快。具有过滤 sEMG 和延迟运动学信号的 RNN 模型特别适用于肌肉骨骼模拟和生物机电设备控制中的应用。