Can a Machine Feel Vibrations?: A Framework for Vibrotactile Sensation and Emotion Prediction via a Neural Network
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arXiv
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| Auteurs principaux: | , , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866913673832824832 |
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| author | Lim, Chungman Kim, Gyeongdeok Kang, Su-Yeon Seifi, Hasti Park, Gunhyuk |
| author_facet | Lim, Chungman Kim, Gyeongdeok Kang, Su-Yeon Seifi, Hasti Park, Gunhyuk |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_00268 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Can a Machine Feel Vibrations?: A Framework for Vibrotactile Sensation and Emotion Prediction via a Neural Network Lim, Chungman Kim, Gyeongdeok Kang, Su-Yeon Seifi, Hasti Park, Gunhyuk Human-Computer Interaction 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. |
| title | Can a Machine Feel Vibrations?: A Framework for Vibrotactile Sensation and Emotion Prediction via a Neural Network |
| topic | Human-Computer Interaction |
| url | https://arxiv.org/abs/2502.00268 |