A Direct Collocation method for optimization of EMG-driven wrist muscle musculoskeletal model

EMG-driven musculoskeletal model has been broadly used to detect human intention in rehabilitation robots. This approach computes muscle-tendon force and translates it to the joint kinematics. However, the muscle-tendon parameters of the musculoskeletal model are difficult to measure in vivo and varied across subjects. In this study, a direct collocation (DC) method is proposed to optimize the subject-specific parameters in a wrist musculoskeletal model. The resultant optimized parameters are used to estimate the wrist fiexion/extension motion. The estimation performance is compared with the parameters optimized by the genetic algorithm. Experiment results show that the DC methods have a similar performance compared with GA, in which the mean correlation are 0.96 and 0.93 for the genetic algorithm and DC method respectively. But the direction collocation method requires less optimization time.

Automated summary

The study proposes a direct collocation method to optimize subject-specific parameters in a wrist musculoskeletal model, which is used to estimate wrist flexion/extension motion. The optimized parameters show a similar performance compared to those optimized by genetic algorithm, with the direct collocation method requiring less optimization time.