Abstract:
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 rst 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 classi ed for object type and movement phase. Among three machine learning algorithms (k-Nearest Neighbour, Multinomial Logistic Regression and Support Vector Machines), highest classi cation accuracy was obtained with k-nearest neighbor classi er 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 userspeci c 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 speci cally. Then, same-di erent discrimination and pattern recognition experiments were conducted to evaluate the discrimination and closed-set identi cation 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 a ected 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.|Keywords : Somatosensory, feedback, neuroprosthesis, discrete events, machine learning, vibrotactile, psychophysics.
