Development of a Simulation Tool for Upper Extremity Prostheses

Overview

Amputees often choose not to wear prostheses due to marginal performance or may settle for a prosthesis that offers only cosmetic improvement, but lacks function. A simulation tool consisting of a robotics-based human body model (RHBM) to predict functional motions, and integrated modules for aid in prescription, training, comparative study, and determination of design parameters of upper extremity prostheses will be developed. The main objective of collecting and analyzing human movement during several common tasks is to optimize and validate the robotics based human model. The range of motion data of subjects performing activities of daily living such as opening a door, turning a wheel, grooming, eating, bilateral lifting, as well as recreational and sport activities such as swinging a baseball bat, and golf club will be analyzed. This motion analysis data will also be used to compare data between four groups: a control group (n=10), a braced group simulating prosthesis use (n=10), a group wearing a transradial prosthesis (n=10) and a group wearing a transhumeral prosthesis (n =10).

Data will be collected by an 8 camera Vicon© motion analysis system during one 3-4 hour testing period. Forty-five reflective markers will be attached to subjects skin and clothing via a double sided adhesive electrode collar. The cameras work on an infrared spectrum and the markers are passive reflective spheres. Relations between marker positions and anatomical / known positions on the body are used to calculate the positions of body segments.This analysis will provide information on movement strategies, compensatory motion, and socket movement associated with the selected tasks for transradial and transhumeral prostheses. Differences in the range of motion of the prostheses users and control subjects will be calculated to determine compensatory motion. The movement of the prosthesis's socket as a function of task and other factors will also be measured. Measured data will be used to minimize error in the simulation of the upper body movement. Knowledge of human motor function given in the recorded data can be extended to give insight to movement parameters when designing new prosthetics. Simulations will be optimized to the collected data using a regressive best fit method.