Inverse Kinematics of Human Arm Based on Multisensor Data Integration

The paper considers a technique for computation of the inverse kinematic model of the human arm. The approach is based on measurements of the hand position and orientation as well as acceleration and angular rate of the upper arm segment. A quaternion description of orientation is used to avoid singularities in representations with Euler angles. A Kalman filter is designed to integrate sensory data from three different types of sensors. The algorithm enables estimation of human arm posture, which can be used in trajectory planning for rehabilitation robots, evaluation of motion of patients with movement disorders, and generation of virtual reality environments.