Modeling Upper Body Kinematics While Using a Transradial Prosthesis

The prostheses used by the majority of persons with upper limb amputations today offer a limited range of motion. Relative to anatomical joints transradial (below the elbow) prosthesis users lose at least two of the three degrees of freedom provided by the wrist and forearm. Some myoeletric prostheses currently allow for forearm pronation and supination (rotation about an axis parallel to the forearm) and the operation of a powered prosthetic hand. Body-powered prostheses, incorporating hooks and other cable driven terminal devices, have even fewer active degrees of freedom. In order to perform activities of daily living, an amputee must use a greater than normal range of movement from other anatomical body joints to compensate for the loss of movement caused by the amputation. By studying this compensatory motion of prosthetic users, the mechanics of how they adapt to the loss of range of motion in a given limb and specific tasks were analyzed. The purpose of this study is to create a robotic based kinematic model that can predict the compensatory motion of a given task using given subject data in select tasks. The tasks used in this study are the activities of daily living: opening a door, drinking from a cup, lifting a box, and turning a steering wheel. For the model the joint angles necessary to accomplish a task are calculated by a simulation for a set of prostheses and tasks. The simulation contains a set of configurations that are represented by parameters that consist of the joint degrees of freedom provided by each prosthesis, and a set of task information that includes joint constraints and trajectories. In the simulation the hand or prosthesis follows the trajectory to perform the task. Analysis of tasks is done by attaching prosthetic constraints to one of the arms of the upper body model in the simulation, other arm maintains an anatomical configuration. By running the model through this simulation with different configurations the compensatory motions were found. Results can then be used to select the best prosthesis for a given user, design prostheses that are more effective at selected tasks, and demonstrate some possible compensations given a set of residual joint limitations with certain prosthetic components, by optimizing the configuration of the prostheses to improve their performance