Improved feature detection over large force ranges using history dependent transfer functions

In this paper we present a history dependent transfer function (HDTF) as a possible approach to enable improved haptic feature detection in high dynamic range (HDR) volume data. The HDTF is a multi-dimensional transfer function that uses the recent force history as a selection criterion to switch between transfer functions, thereby adapting to the explored force range. The HDTF has been evaluated using artificial test data and in a realistic application example, with the HDTF applied to haptic protein-ligand docking. Biochemistry experts performed docking tests, and expressed that the HDTF delivers the expected feedback across a large force magnitude range, conveying both weak attractive and strong repulsive protein-ligand interaction forces. Feature detection tests have been performed with positive results, indicating that the HDTF improves the ability of feature detection in HDR volume data as compared to a static transfer function covering the same range.