Movement Onset Detection and Target Estimation for Robot-Aided Arm Training

作者：Riener Robert^*; Novak Domen

来源：Automatisierungstechnik, 2015, 63(4): 286-298.

DOI：10.1515/auto-2015-0004

• Intention detection
• physical human-robot interaction
• rehabilitation robotics
• sensor fusion

This paper presents amotion intention estimation algorithm that is based on the recordings of joint torques, joint positions, electromyography, eye tracking and contextual information. It is intended to be used to support a virtual-reality-based robotic arm rehabilitation training. The algorithm first detects the onset of a reaching motion using joint torques and electromyography. It then predicts the motion target using a combination of eye tracking and context, and activates robotic assistance toward the target. The algorithm was first validated offline with 12 healthy subjects, then in a real-time robot control setting with 3 healthy subjects. In offline crossvalidation, onset was detected using torques and electromyography 116 ms prior to detectable changes in joint positions. Furthermore, it was possible to successfully predict a majority of motion targets, with the accuracy increasing over the course of the motion. Results were slightly worse in online validation, but nonetheless show great potential for real-time use with stroke patients.

• 出版日期2015-4