Perceptual Deadband for Haptic Data Compression: Symmetric or Asymmetric?

In the literature, perceptual deadband approach based on the Weber's law of perception has been employed to reduce haptic data (i.e., force) rate for a typical teleoperation application. The approach selects only those samples for transmission which lie outside the perceptual deadband. The existing structure of the deadband has linear decision boundaries and assume that the just noticeable differences (JNDs) for increasing and decreasing change in a reference force stimulus are similar. This paper questions this assumption and searches for an asymmetric perceptual deadband using a data-driven approach. For the purpose, we design an experimental setup for collecting human haptic responses (perceived and non-perceived) of several users for a force range [3, 5] N. A machine learning classifier inspired from the Weber's law is trained to predict the labels of the responses and define a generalized linear perceptual deadband for each user. The results show that the generalized deadband do provide different increasing and decreasing JND (i.e., pointing towards asymmetry in force perception), but fails to improve the data reduction significantly as compared to the existing symmetric one.