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Applications of Machine Learning in Haptic Perception

Haptic data transmission has been a recent area of interest in the haptics research community for the past few years. Unlike video and audio transmission, the haptic data transmission is bidirectional in nature. It forms a global control loop over a communication network. In order to maintain the stability of the control loop, only a small amount of communication delay (1-5 ms) is permitted, which is smaller compared to the permissible limit required for typical applications involving audio and video. Therefore, only a small number of samples can be incorporated in a packet, which leads to a high packet rate. The high packet rate may cause network congestion over existing network protocols like Internet, causing loss of the packets and instability of the global control loop. Thus, it is required to reduce the packet rate while maintaining the quality of the perception. In recent past, many researchers have used perceptually adaptive sampling scheme based on the Weber’s law for the purpose. According to Weber’s law, a force signal will be perceived as being a different one from the reference signal if its relative difference with respect to the reference force exceeds a certain threshold value. This threshold is named as the Weber threshold or the just noticeable difference (JND). Hence, in case of perceptually adaptive sampling, a kinesthetic force signal will be sampled at points where the relative difference exceeds the JND. It means that this approach transmits only perceptually significant sample points, and the JND determines their perceptual significance. Hence, it is required to study and identify different adaptive sampling strategies for haptic signals.