Upper limb rehabilitation trajectory optimization based on artificial immune genetic algorithm

In this paper, we focus on the human upper limb multi-joints trajectory optimization under the constraints of the stroke patients with hemiplegia. A three-dimensional motion trajectory planning method based on artificial immune genetic algorithm is put forward. We combine the minimal effort criterion with three-dimensional motion dynamics model to solve the objective function of the multi-joint motion trajectory. So, we convert point-to-point motion trajectory optimization into solving the joint angle value. Using artificial immune idea, we design a optimization algorithm to solve the optimal trajectory of the upper limb movement. The simulations reveal that the immune algorithm has faster convergence to global optimum, and the trajectory is smooth. Velocity and acceleration curves are stable and without saltaton avoiding the quick starts and stops, which meet the standards of upper limb movement characteristics. The method in this paper can optimize the upper limb rehabilitation trajectory more effective and faster.