Detecting and Identifying Tactile Gestures using Deep Autoencoders, Geometric Moments and Gesture Level Features

摘要

While several sensing modalities and transduction approaches have been developed for tactile sensing in robotic skins, there has been much less work towards extracting features for or identifying high-level gestures performed on the skin. In this paper, we investigate using deep neural networks with hidden Markov models (DNN-HMMs), geometric moments and gesture level features to identify a set of gestures performed on robotic skins. We demonstrate that these features are useful for identifying gestures, and predict a set of gestures from a 14-class dataset with 56% accuracy, and a 7-class dataset with 71% accuracy.