The Tactile Internet will enable users to physically explore remote environments and to make their skills available across distances. An important technological aspect in this context is the acquisition, compression, transmission, and display of haptic information. In this paper, we present the fundamentals and state of the art in haptic codec design for the Tactile Internet. The discussion covers both kinesthetic data reduction and tactile signal compression approaches. We put a special focus on how limitations of the human haptic perception system can be exploited for efficient perceptual coding of kinesthetic and tactile information. Further aspects addressed in this paper are the multiplexing of audio and video with haptic information and the quality evaluation of haptic communication solutions. Finally, we describe the current status of the ongoing IEEE standardization activity P1918.1.1 which has the ambition to standardize the first set of codecs for kinesthetic and tactile information exchange across communication networks.

When a user scratches a hand-held rigid tool across an object surface, an acceleration signal can be captured, which carries relevant information about the surface material properties. More importantly, such haptic acceleration signals can be used together with surface images to jointly recognize the surface material. In this paper, we present a novel deep learning method dealing with the surface material classification problem based on a fully convolutional network, which takes the aforementioned acceleration signal and a corresponding image of the surface texture as inputs. Compared to the existing surface material classification solutions which rely on a careful design of hand-crafted features, our method automatically extracts discriminative features utilizing advanced deep learning methodologies. Experiments performed on the TUM surface material database demonstrate that our method achieves state-of-the-art classification accuracy robustly and efficiently.

When a tool is used to tap onto an object or it is dragged over the object surface, vibrations are induced in the tool that can be captured using acceleration sensors. Based on these signals, this paper presents an approach for tool-mediated surface classification which is robust against varying scan-time parameters. We examine freehand recordings of 69 textures and propose a classification system that uses perception-related features such as hardness, roughness and friction as well as selected features adapted from speech recognition such as modified cepstral coefficients. We focus on mitigating the effect of varying contact force and hand speed conditions on these features as a prerequisite for a robust machine-learning-based approach for surface classification. Our system works without explicit scan force and velocity measurements. Experimental results show that our proposed approach allows for successful classification of surface textures under varying freehand movement conditions. The proposed features lead to a classification accuracy of 95% when combined with a Naive Bayes Classifier.