Human-Like Trajectory Planning Based on Postural Synergistic Kernelized Movement Primitives for Robot-Assisted Rehabilitation
Zemin Liu, Qingsong Ai, Haojie Liu, Wei Meng, Quan Liu
Abstract
The motor synergy pattern is an intrinsic characteristic found in natural human movements, particularly in the upper limb. It is essential to improve the multijoint coordination ability for stroke patients by integrating the synergy pattern into rehabilitation tasks and trajectory design. However, current robot-assisted rehabilitation systems tend to overlook the incorporation of a multijoint synergy model. This article proposes postural synergistic kernelized movement primitives (PSKMP) method for the human-like trajectory planning of robot-assisted upper limb rehabilitation. First, the demonstrated trajectory obtained from the motion capture system is subject to principal component analysis to extract postural synergies. Then, the PSKMP is proposed by kernelizing the postural synergistic subspaces with the kernel treatment to preserve human natural movement characteristics. Finally, the rehabilitation trajectory accord with human motion habits can be generated based on generalized postural synergistic subspaces. This approach has undergone practical validation on an upper limb rehabilitation robot, and the experimental results show that the proposed method enables the generation of human-like trajectories adapted to new task points, in accordance with the natural movement style of human. This method holds great significance in promoting the recovery of coordination ability of stroke patients.