High-fidelity tactile rendering offers significant potential for improving the richness and immersion of touchscreen interactions. This study focuses on a quantitative description of tactile rendering fidelity using a custom-designed hybrid electrovibration and mechanical vibration (HEM) device. An electrovibration and mechanical vibration (EMV) algorithm that renders 3D gratings with different physical heights was proposed and shown to achieve 81% accuracy in shape recognition. Models of tactile rendering fidelity were established based on the evaluation of the height discrimination threshold, and the psychophysical-physical relationships between the discrimination and reference heights were well described by a modification of Weber’s law, with correlation coefficients higher than 0.9. The physiological-physical relationship between the pulse firing rate and the physical stimulation voltage was modeled using the Izhikevich spiking model with a logarithmic relationship.

The interpretation of tactile stimuli empowers humans to identify substances, distinguish materials, and engage in tactile communication. For stimulus design in human-computer interaction, objective similarity measures improve efficiency and save costs. Inspired by the fact that biological systems are robust in recognizing multimedia stimuli, we propose a neuromorphic method for similarity measurement. The method is divided into two steps. First, tactile information is translated into biological representations by mimicking a low-threshold mechanoreceptor through a physiological neuronal model. Then, three measures are nominated to assess the similarity of neural spike trains from the following perspectives: interval spike counting, temporal matching, and vector space embedding. Regression analysis showed that the linearity of these measures was significant, indicating that the filtering ability of the physiological neuron model is robust. One of the measures is selected for comparison with the signal-to-noise ratio, structural similarity, and hybrid metric. The results suggest that the correlation between the predictions of our method and the subjective evaluation is stable, above 0.9 for each experimental stimulus. We achieve a mutual interpretation between quantitative measures of vibrotactile similarity and subjective cognitive outcomes. Furthermore, the feasibility of this method in material classification has been substantiated through an exploratory experiment.

Adequate-data collection could enhance the realism of surface texture haptic online-rendering or offline-playback. A parallel challenge is how to reduce communication delays and improve storage space utilization. Based on the similarity of the short-term amplitude spectrumtrend, this paper proposes a frequency-domain compression method. A compression framework is designed, firstly to map the amplitude spectrum into a trend similarity grayscale image, compress it with the stillpicture-compression method, and then to adaptively encode the maximum amplitude and part of the initial phase of each time-window, achieving the final compression. The comparison between the original signal and the recovered signal shows that when the time-frequency similarity is 90%, the average compression ratio of our method is 9.85% in the case of a single interact point. The subjective score for the similarity reached an excellent level, with an average score of 87.85. Our method can be used for offline compression of vibrotactile data. For the case of multi-interact points in space, the trend similarity grayscale image can be reused, and the compression ratio is further reduced.