Classification of texture and frictional condition at initial contact by tactile afferent responses

Adjustments to friction are crucial for precision object handling in both humans and robotic manipulators. The aim of this work was to investigate the ability of machine learning to disentangle concurrent stimulus parameters, such as normal force ramp rate, texture and friction, from the responses of tactile afferents at the point of initial contact with the human finger pad. Three textured stimulation surfaces were tested under two frictional conditions each, with a 4 N normal force applied at three ramp rates. During stimulation, the responses of fourteen afferents (5 SA-I, 2 SA-II, 5 FA-I, 2 FA-II) were recorded. A Parzen window classifier was used to classify ramp rate, texture and frictional condition using spike count, first spike latency or peak frequency from each afferent. This is the first study to show that ramp rate, texture and frictional condition could be classified concurrently with over 90% accuracy using a small number of tactile sensory units.