Solving EMG-force relationship using Particle Swarm Optimization

The Particle Swarm Optimization (PSO) algorithm is applied to the problem of “load sharing” among muscles acting on the same joint for the purpose of estimating their individual mechanical contribution based on their EMG and on the total torque. Compared to the previously tested Interior-Reflective Newton Algorithm (IRNA), PSO is more computationally demanding. The mean square error between the experimental and reconstructed torque is similar for the two algorithms. However, IRNA requires multiple initializations and tighter constraints found by trial-and-errors for the input variables to find a suitable optimum which is not the case for PSO whose initialization is random.