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This paper proposes a novel prosthetic hand control method that incorporates spatial information of target objects obtained with a RGB-D sensor into a myoelectric control procedure. The RGB-D sensor provides not only two-dimensional (2D) color information but also depth information as spatial cues on target objects, and these pieces of information are used to classify objects in terms of shape features. The shape features are then used to determine an appropriate grasp strategy/motion for control of a prosthetic hand. This paper uses a two-channel image format for classification, which contains grayscale and depth information of objects, and the image data is classified with a deep convolutional neural network (DCNN). Compared with previous studies based only on 2D color images, it is expected that the spatial information would improve classification accuracy, and consequently better grasping decision and prosthetic control can be achieved. In this study, a dataset of image pairs, consisting of grayscale images and their corresponding depth images, has been created to validate the proposed method. This database includes images of simple three-dimensional (3D) solid objects from six categories, namely, triangular prism, triangular pyramid, quadrangular prism, rectangular pyramid, cone, and cylinder. Image classification experiments were conducted with this database. The experimental results indicate that spatial information possesses high potential in classifying shape features of objects.

Object Shape Classification Using Spatial Information in Myoelectric Prosthetic Control