A biomimetic soft fingertip applicable to haptic feedback systems for texture identification

Humans recognize textures using the tactile data obtained from the human somatosensory system. Recognition of textures allows humans discriminate objects and materials. Moreover, by understanding the object's or material's texture, the human intuitively estimates roughness and the friction properties of the object or the material. This ability is necessary for object manipulative tasks. Likewise artificial haptic systems too, should have the ability to encode textures and feedback those data to haptic applications such as haptic displays. In this paper a biomimetic soft fingertip sensor that can be used in above haptic systems is introduced. The fingertip has the ability to detect force and vibration modalities. We propose three features calculated from the covariance signal of two adjacent accelerometers in the fingertip to use in texture identification. The covariance signal is transformed using Discrete Wavelet Transform (DWT) and the three features mentioned below are calculated. The mean and variance of the approximate signal, and the energies of the detailed signal are chosen as features. Then, the proposed features were validate by using those in an Artificial Neural Network (ANN) to classify seven wood samples. The results showed a 65% success rate in classifying wood samples and that the proposed features are acceptable to encode textures.