Institute of Rehabilitation Engineering and Technology, University of Shanghai for Science and Technology, Shanghai, China.
School of Mechanical Engineering, University of Shanghai for Science and Technology, Shanghai, China.
Technol Health Care. 2023;31(1):197-204. doi: 10.3233/THC-220190.
Human gait involves activities in nervous and musculoskeletal dynamics to modulate joint torques with time continuously for adapting to varieties of walking conditions.
The goal of this paper is to estimate the joint torques of lower limbs in human gait based on Gaussian process.
The potential uses of this study include optimization of exoskeleton assistance, control of the active prostheses, and modulating the joint torque for human-like robots. To achieve this, Gaussian process (GP) based data fusion algorithm is established with joint angles as the inputs.
The statistic nature of the proposed model can explore the correlations between joint angles and joint torques, and enable accurate joint-torque estimations. Experiments were conducted for 5 subjects at three walking speed (0.8 m/s, 1.2 m/s, 1.6 m/s).
The results show that it is possible to estimate the joint torques at different scenarios.
人类步态涉及神经和肌肉骨骼动力学活动，以随时间连续调节关节扭矩，以适应各种行走条件。
本文的目的是基于高斯过程估计人体步态中的下肢关节扭矩。
本研究的潜在用途包括优化外骨骼辅助、主动假肢控制以及为类人机器人调节关节扭矩。为此，建立了基于高斯过程 (GP) 的数据融合算法，以关节角度作为输入。
所提出模型的统计特性可以探索关节角度和关节扭矩之间的相关性，并实现准确的关节扭矩估计。在三种步行速度（0.8 m/s、1.2 m/s、1.6 m/s）下对 5 名受试者进行了实验。
结果表明，在不同场景下估计关节扭矩是可行的。
