Can a Machine Feel Vibrations?: Predicting Roughness and Emotional Responses to Vibration Tactons via a Neural Network

Authors:
Publisher website
Google Scholar
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 roughness 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.
Funding Information
  • Villum Fonden (VIL50296)
  • National Science Foundation (2339707)
  • Institute of Information and Communications Technology Planning and Evaluation (2019-0-01842)
  • Artificial Intelligence Graduate School Program
  • Culture, Sports and Tourism R&D Program
  • Ministry of Culture, Sports and Tourism in 2023 (RS-2023-00226263)

This publication has 73 references indexed in Scilit: