An Embedded System for Collection and Real-Time Classification of a Tactile Dataset

Tactile perception of the material properties in real-time using tiny embedded systems is a challenging task and of grave importance for dexterous object manipulation such as robotics, prosthetics and augmented reality. As the psychophysical dimensions of the material properties cover a wide range of percepts, embedded tactile perception systems require efficient signal feature extraction and classification techniques to process signals collected by tactile sensors in real-time. For this purpose, we developed two embedded systems, one that served as a vibrotactile stimulator system and one that recorded and classified the vibrotactile signals collected by its sensors. The quality of the collected data was first verified offline using Fourier transform for feature extraction and then applying powerful machine learning classifiers such as support vector machines and neural networks. We implemented the proposed memory-less signal feature extraction method in order to achieve real-time processing as the data is being collected. The experimental results have shown that the proposed method significantly reduces the computational complexity of feature extraction and still has led to high classification accuracy even when fed to the less complex classifiers such as random forests that can be easily implemented on embedded systems. Finally, we have also shown that low-cost, highly accurate, and real-time tactile texture classification can be achieved using the proposed approach with an ensemble of sensors.