Development of a prosthetic hand control system Based on general object recognition analysis of recognition accuracy during approach phase

This paper proposes a novel control method which combines an EMG pattern classifier with a vision-based object classifier to control various motions of a prosthetic hand. A deep convolutional neural network is adopted for the object recognition, and the posture of the prosthetic hand is controlled based on the recognition result of the object. To verify the validity of the proposed control method, the experiment was executed with 25 target objects. 3000 images for each target object were collected during the approach phase of hand motion to the object. High recognition performance was confirmed with an accuracy over 80%, although the misclassification was observed at the early phase of the approach motion. These results revealed that the proposed method has high potential to control various motions of the prosthetic hand.