Exoskeleton for motion assistance has obtained more and more attention due to its advantages in rehabilitation and assistance for daily life. This research designed an estimation method of human joint torque by the kinetic human–machine interaction between the operator’s elbow joint torque and the output of exoskeleton. The human elbow joint torque estimation was obtained by back propagation (BP) neural network with physiological and physical input elements including shoulder posture, elbow joint-related muscles activation, elbow joint position, and angular velocity. An elbow-powered exoskeleton was developed to verify the validity of the human elbow joint torque estimation. The average correlation coefficients of estimated and measured three shoulder joint angles are 97.9%, 96.2%, and 98.1%, which show that estimated joint angles are consistent with the measured joint angle. The average root-mean-square error between estimated elbow joint torque and measured values is about 0.143[Formula: see text]N[Formula: see text]m. The experiment results proved that the proposed strategy had good performance in human joint torque estimation.

中文翻译：

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一种肘外骨骼控制的人体关节力矩估计方法

运动辅助外骨骼因其在康复和日常生活辅助方面的优势而受到越来越多的关注。本研究设计了一种通过操作者肘关节扭矩与外骨骼输出之间的动力学人机交互来估计人体关节扭矩的方法。人体肘关节扭矩估计是通过反向传播（BP）神经网络获得的，其生理和物理输入元素包括肩部姿势、肘关节相关肌肉激活、肘关节位置和角速度。开发了一种肘动力外骨骼，以验证人体肘关节扭矩估计的有效性。估计和测量的三个肩关节角度的平均相关系数分别为 97.9%、96.2% 和 98.1%，这表明估计的关节角度与测量的关节角度一致。肘关节扭矩估计值与实测值的均方根误差约为0.143[公式：见正文]N[公式：见正文]m。实验结果证明，该策略在人体关节扭矩估计中具有良好的性能。