Optimization-Based Posture Prediction For Analysis Of Box Lifting Tasks

New methods for optimization-based posture prediction with external forces are presented and tested. The proposed approach incorporates prediction of 113 degrees of freedom including global position and orientation of the body as well as foot position, while considering balance. Postures and joint torques are successfully predicted and compared to motion-capture data and literature-based data respectively. This approach is applied to a box-lifting task and provides a robust tool for studying human performance and for preventing injuries.