Haptic Data Compression Based on a Linear Prediction Model and Quadratic Curve Reconstruction

In this paper, a new haptic data compression technique is presented. The algorithm partitions haptic data samples into subsets while relying on knowledge from human haptic perception. To reduce the number of data subsets, a prediction model based on a tangential direction concept is derived that adapts to local geometric changes of haptic signals. Furthermore, to improve signal approximation precision, each haptic data subset is fitted by a quadratic curve. Accordingly, only the coefficents of the quadratic curves are encoded. Experiments are performed on datasets acquired using a six-degrees-of-freedom haptic-enabled telepresence system. The experimental results demonstrated that the proposed haptic data compression algorithm can potentially outperform related methods in the literature.