Psychophysical evaluation of a sensory feedback system for prosthetic hands

In this study, a vibrotactile sensory feedback system was designed and tested in accordance with the discrete event-driven sensory feedback control paradigm. Novel approaches were applied in terms of data processing and psychophysical characterization. As the first part, the sensing and signal processing system was designed. Therefore, a robotic hand was equipped with force and bend sensors by mimicking receptors in human hand. The sensor data was recorded during a cylindrical grasping task, and classified for object type and movement phase. Among three machine learning algorithms (k-Nearest Neighbour, Multinomial Logistic Regression and Support Vector Machines), highest classification accuracy was obtained with k-nearest neighbor classifier and the results were promising for the subsequent work. In the second part, the sensory feedback system was designed using two vibrotactile actuators and a user-specific calibration method was presented. The actuators were placed on the upper arms of 10 able-bodied participants. A psychophysical characterization procedure was applied to determine the stimulation amplitudes for each participant specifically. Then, same-different discrimination and pattern recognition experiments were conducted to evaluate the discrimination and closed-set identification of stimuli with varying parameters. Finally, discrete-event driven feedback experiments were run by mapping the parameters of the stimuli to the discrete events related to class labels representing object/movement type. According to the results, the psychophysical characterization procedure was reliable. On the other hand, the performance in the complex tasks was not affected by the psychophysical variations across participants. Experimental results showed that the system can be used to provide object-type and movement-type related information in daily use of prosthetic devices.