Abstract
To realize an electric shoulder prosthesis arm system that can be used without long-term training, we developed an intuitive shoulder prosthesis system. The developed system consisted of four degrees of freedom joints and control system adapting a user through machine learning technique and surface myoelectric potential of the trunk. We measured the surface myoelectric potential of the trunk of healthy subjects at multiple points and analyzed by using a principal component analysis to identify the proper myoelectric potential measurement part of the trunk. As a result, it turned out that the proper myoelectric potential measurement parts were distributed in the chest and back. In addition, performance of our developed system was evaluated by the pick-and-place task in three-dimensional space. In the task, developed system was controlled by myoelectric potential obtained from the proper myoelectric potential measurement part. As a result of the experiment, our developed shoulder disarticulation prosthesis was able to grasp and move the object in vertical and horizontal directions with a minimum learning time of 35 seconds.
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