Application of Dynamic Weight Particle Swarm Optimization with Cross Factor in Joint Calibration

The alignment of inertial measurement units(IMUs) to segment is an important step in inertial motion capture, which directly affects whether the imu data can fully represent the motion of the segment. Inspired by the gene crossover and mutation of Genetic Algorithm(GA), we propose a dynamic inertial weighted particle swarm optimization algorithm with cross factor to solve the joint constraint problem, and compared our algorithm with Particle Swarm Optimization(PSO) and Dynamic Inertial Weighted Particle Swarm Optimization(DPSO) algorithms to show the superiority of our algorithm during human lower limb movements. The experiment shows that introduced the random cross mechanism between particles with larger fitness and only the effective cross retained, makes the new algorithm show better search ability and convergence effect in this project, the stability and effectiveness are also improved. Our current work provides a good support for accurate calculation of joint angles in the future.