Improvement of a neural network-based motion generator with bimanual coordination for upper limb prosthesis

We propose to redesign a neural network used as a motion generator with bimanual coordination for upper limb prosthesis in order to improve its learning capability. We assumed that the wearer of the prosthesis was a unilateral amputee. In our previous work, we proposed a prosthesis control system using a neural network that learned bimanual coordination in order to implement smooth motion with both hands. However, the previously proposed system has the problem that a neural network cannot generate the desired motion of the prosthesis in special cases. The reason is that the motion generator calculates the desired posture of the prosthesis from the current posture of the healthy arm only, regardless of the current posture of the prosthesis. We propose to use the current posture of both the healthy arm and the prosthesis as neural network inputs in order to solve this problem. In this article, we show that a single neural network whose input was the current posture of both arms could learn the relationships of the coordinated motions of holding boxes of different sizes, and the newly proposed system can calculate the desired motion of the prosthesis in special cases through computer simulations.