Neuro-Robotics Lab, Department of Robotics, Graduate School of Engineering, Tohoku University, Sendai 980-8579, Japan.
Sensors (Basel). 2023 Mar 22;23(6):3331. doi: 10.3390/s23063331.
One of the fundamental limitations in human biomechanics is that we cannot directly obtain joint moments during natural movements without affecting the motion. However, estimating these values is feasible with inverse dynamics computation by employing external force plates, which can cover only a small area of the plate. This work investigated the Long Short-Term Memory (LSTM) network for the kinetics and kinematics prediction of human lower limbs when performing different activities without using force plates after the learning. We measured surface electromyography (sEMG) signals from 14 lower extremities muscles to generate a 112-dimensional input vector from three sets of features: root mean square, mean absolute value, and sixth-order autoregressive model coefficient parameters for each muscle in the LSTM network. With the recorded experimental data from the motion capture system and the force plates, human motions were reconstructed in a biomechanical simulation created using OpenSim v4.1, from which the joint kinematics and kinetics from left and right knees and ankles were retrieved to serve as output for training the LSTM. The estimation results using the LSTM model deviated from labels with average scores (knee angle: 97.25%, knee moment: 94.9%, ankle angle: 91.44%, and ankle moment: 85.44%). These results demonstrate the feasibility of the joint angle and moment estimation based solely on sEMG signals for multiple daily activities without requiring force plates and a motion capture system once the LSTM model is trained.
人体生物力学的一个基本局限性是，我们无法在不影响运动的情况下直接获得自然运动中的关节力矩。然而，通过使用外力板进行逆动力学计算，可以估计这些值，外力板只能覆盖板的一小部分区域。这项工作研究了长短期记忆 (LSTM) 网络，用于在学习后不使用力板的情况下预测人类下肢在进行不同活动时的运动学和运动学。我们从 14 条下肢肌肉测量表面肌电图 (sEMG) 信号，从三组特征生成一个 112 维输入向量：均方根、绝对值和每个肌肉的六阶自回归模型系数参数。使用运动捕捉系统和力板记录的实验数据，在使用 OpenSim v4.1 创建的生物力学模拟中重建人体运动，从该模拟中检索左膝和右膝以及脚踝的关节运动学和动力学，作为训练 LSTM 的输出。使用 LSTM 模型的估计结果与标签的平均得分（膝关节角度：97.25%，膝关节力矩：94.9%，踝关节角度：91.44%，踝关节力矩：85.44%）存在偏差。这些结果表明，一旦 LSTM 模型经过训练，仅基于 sEMG 信号就可以对多种日常活动进行关节角度和力矩估计，而无需使用力板和运动捕捉系统。
