Chungman Lim

1. Recent improvements in commercial AR/VR systems, such as Vision Pro (Apple) and Meta Quest 3 (Meta), have enabled high-quality visual information to users. These systems also allow for free-hand...

Thermal–vibration feedback has been reported to elicit the thermal referral phenomenon, offering new possibilities for enhancing user experience. Yet, little research has explored whether thermal referral occurs in the presence of texture-slip feedback. To address this, we developed a custom haptic device capable of delivering integrated thermal and slip feedback to the middle and distal phalanges of an index finger. We primarily estimated the thermal referral occurrence under passive and active touch conditions across two different perceptual experiments, while each further examined its impact on texture perceptions and user experiences (UX). In Perceptual Experiment 1 (passive), we observed the highest occurrence of thermal referral at 40°C (0.86 on a 0–1 scale), and found that referral increased the median of perceived roughness from 43.5 to 60 on a 0—100 scale. In Perceptual Experiment 2 (active), the kinesthetic cues significantly enhanced thermal referral, with the occurrence probability at 24°C increasing to 0.86 compared to 0.51 (0—1 scale) in the first study. In addition, high UX ratings demon strated the applicability of our device to temperature-integrated virtual texture interactions. Our findings expand the scope of thermal referral and highlight the potential for enabling VR users to interact with virtual surfaces that provide temperature cues.

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.

Mid-air ultrasound technology offers new design opportunities for contactless tactile patterns (i.e., Tactons) in user applications. Yet, few guidelines exist for making ultrasound Tactons easy to distinguish for users. In this paper, we investigated the distinguishability of temporal parameters of ultrasound Tactons in five studies (n=72 participants). Study 1 established the discrimination thresholds for amplitude-modulated (AM) frequencies. In Studies 2–5, we investigated distinguishable ultrasound Tactons by creating four Tacton sets based on mechanical vibrations in the literature and collected similarity ratings for the ultrasound Tactons. We identified a subset of temporal parameters, such as rhythm and low envelope frequency, that could create distinguishable ultrasound Tactons. Also, a strong correlation (mean Spearman’s ρ =0.75) existed between similarity ratings for ultrasound Tactons and similarities of mechanical Tactons from the literature, suggesting vibrotactile designers can transfer their knowledge to ultrasound design. We present design guidelines and future directions for creating distinguishable mid-air ultrasound Tactons.