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In this paper, we demonstrate how automatic grasp selection can be achieved by placing a camera in the palm of a prosthetic hand and training a convolutional neural network on images of objects with corresponding grasp labels. Our labeled dataset is built from common graspable objects curated from the ImageNet dataset and from images captured from our own camera that is placed in the hand. We achieve a grasp classification accuracy of 93.2% and show through real-time grasp selection that usingdoi:10.1109/embc.2016.7590732 pmid:28261002 pmcid:PMC5325038 fatcat:pbbxxnyr4rfele7wkfk5weomka