Joint vibrotactile coding for machine recognition and human perception - Ying FANG; Yiwen XU; Tiesong ZHAO (Tongxin xuebao)
In order to accurately transmit the content meaning of vibrotactile signals and achieve intelligent recognition and signal reconstruction, a joint vibrotactile coding scheme for machine recognition and human perception was proposed.At the encoding end, the original three-dimensional vibrotactile signals were converted into one-dimensional signals.Then the semantic information of the signals was extracted using a short-time Fourier transform before being effectively compressed and transmitted.At the decoding end, a fully convolutional neural network was used to intelligently recognize based on the semantic information.The difference between the original signals and the reconstructed signals based on semantic information was used as compensation for the semantic information, and the quality of the reconstructed signals was gradually improved to meet human perceptual needs.The experimental results show that the proposed scheme achieve tactile recognition with semantic information at a lower bit rate while improving the compression efficiency of tactile data, thus satisfying human perceptual needs.