• Dynamics simulation of human box delivering task

      Owens, Paul Davis; Xiang, Yujiang; Peterson, Rorik; Chen, Cheng-fu (2018-05)
      The dynamic optimization of a box delivery motion is a complex task. The key component is to achieve an optimized motion associated with the box weight, delivering speed, and location. This thesis addresses one solution for determining the optimal delivery of a box. The delivering task is divided into five subtasks: lifting, transition step, carrying, transition step, and unloading. Each task is simulated independently with appropriate boundary conditions so that they can be stitched together to render a complete delivering task. Each task is formulated as an optimization problem. The design variables are joint angle profiles. For lifting and carrying task, the objective function is the dynamic effort. The unloading task is a byproduct of the lifting task, but done in reverse, starting with holding the box and ending with it at its final position. In contrast, for transition task, the objective function is the combination of dynamic effort and joint discomfort. The various joint parameters are analyzed consisting of joint torque, joint angles, and ground reactive forces. A viable optimization motion is generated from the simulation results. It is also empirically validated. This research holds significance for professions containing heavy box lifting and delivering tasks and would like to reduce the chance of injury.
    • Maximum weight lifting prediction considering dynamic joint strength

      Rana, G M Rahid uz zaman; Xiang, Yujiang; Chen, Cheng-fu; Peterson, Rorik (2018-05)
      This thesis describes an efficient optimization method for predicting the maximum lifting weight considering dynamic joint strength in symmetric box lifting using a skeletal model. Dynamic joint strength is modeled as a three-dimensional function of joint angle and joint angular velocity based on experimentally obtained joint strength data. The function is further formulated as the joint torque limit constraint in an inverse dynamics optimization formulation to predict the lifting motion. In the proposed optimization formulation, external load is treated as design variables along with joint angle profiles, which are represented by control points of B-spline curves. By using this new formulation, dynamic lifting motion and strategy can be predicted for a symmetric maximum weight box lifting task with given initial and final box locations. Results show that incorporating dynamic strength is critical in predicting the lifting motion in extreme lifting conditions. The prediction outputs in joint space are incorporated in OpenSim software to find out muscles force and activity during the movement. Electromyography data are collected for a regular weight lifting to validate the integration process between the predictive model (joint model) and OpenSim model (muscle model). The proposed algorithm and analysis method based on motion prediction and OpenSim can be further developed as a useful ergonomic tool to protect workers from injury in manual material handling.