Can a Machine Feel Vibrations?: A Framework for Vibrotactile Sensation and Emotion Prediction via a Neural Network

Abstract

Vibrotactile signals offer new possibilities for conveying sensations and emotions in various applications. Yet, designing vibrotactile tactile icons (i.e., Tactons) to evoke specific feelings often requires a trial-and-error process and user studies. To support haptic design, we propose a framework for predicting sensory and emotional ratings from vibration signals. We created 154 Tactons and conducted a study to collect acceleration data from smartphones and roughness, valence, and arousal user ratings (n=36). We converted the Tacton signals into two-channel spectrograms reflecting the spectral sensitivities of mechanoreceptors, then input them into VibNet, our dual-stream neural network. The first stream captures sequential features using recurrent networks, while the second captures temporal-spectral features using 2D convolutional networks. VibNet outperformed baseline models, with 82% of its predictions falling within the standard deviations of ground truth user ratings for two new Tacton sets. We discuss the efficacy of our mechanoreceptive processing and dual-stream neural network and present future research directions.