Quantitative analysis of the human upper-limp kinematic model for robot-based rehabilitation applications

Abstract

Upper-limb robotic rehabilitation systems should inform the therapists for their patients status. Such therapy systems must be developed carefully by taking into consideration real life uncertainties that associate with sensor error. In our paper, we describe a system which is composed of a depth camera that tracks the motion of the patients upper limb, and a robotic manipulator that challenges the patient with repetitive exercises. The goal of this study is to propose a motion analysis system that improves the readings of the depth camera, through the use of a kinematic model that describes the motion of the human arm. In our current experimental set-up we are using the Kinect v2 to capture a participant who performs rehabilitation exercises with the Barrett WAM robotic manipulator. Finally, we provide a numerical comparison among the stand alone measurements from the Kinect v2, the estimated motion parameters of our system and the VICON, which we consider as an error-free ground truth apparatus.