| dc.description.abstract | Abstract. Planar single-degree-of-freedom (DOF) mechanisms are widely used in human rehabilitation devices, yet research on spatial single-DOF mechanisms remains limited. Given the complex spatial nature of human motion, this study proposes a multi-mode spatial RSCR mechanism for gait rehabilitation. Multi-mode mechanisms incorporate adjustable parameters, enabling structural adaptation to different task trajectories. A kinematic model is established, followed by a circuit analysis to ensure trajectory synthesis accuracy. Using Gaussian mixture model clustering, a dataset of human gait trajectories is divided into three clusters, with one representative trajectory regressed from each cluster. A two-stage optimization strategy is implemented: an adaptive reference point-based non-dominated sorting genetic algorithm performs multi-objective optimization to determine optimal adjustable parameters (yA and yFc), while GA-BFGS refines these and additional parameters via single-objective optimization. Simulation results show that the mechanism can accurately reproduce the three representative trajectories by adjusting yA and yFc, with fitting errors of 9.76×10−3 m, 3.35×10−2 m, and 1.09×10−2 m, respectively. These results confirm the feasibility of using distributed optimization for the multi-mode design of RSCR mechanisms. The proposed dual-parameter adjustment method significantly enhances trajectory adaptability, achieving subcentimeter precision in some cases. This work explores a viable path for the development of spatial rehabilitation mechanisms and highlights potential advancements in the multi-mode design of gait rehabilitation systems. | |
