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.

High-fidelity vibration actuators in recent mobile phones allow designers to crowdsource user evaluation of vibrotactile (VT) Tactons. Yet, little work has examined whether online crowdsourcing platforms can provide comparable results to lab studies. To address this question, we conducted two studies with iOS devices in the lab and crowdsourced settings. In Study I, 40 users provided pairwise similarity ratings for 12 VT Tactons that varied in their parameters (e.g., duration). In Study II, 40 new users rated pairwise similarities for 14 Tactons representing different metaphors (e.g., heartbeat). They also rated the Tactons’ match to the metaphors. In both studies, the resulting similarities and perceptual spaces strongly correlated in the lab and crowdsourced settings. Furthermore, 60% of the metaphor ratings were statistically equivalent in the two settings. We discuss the results and outline directions for future work on haptic crowdsourcing.

Perceptual dissimilarities, requiring high-cost user ratings, have contributed to designing well-distinguishable vibrations for associated meaning delivery. Appropriate metrics can reduce the cost, but known metrics in vibration similarity/dissimilarity could not predict them robustly. We propose a physiology-based model (PM) that predicts the perceptual dissimilarities of a given vibration set via two parallel processes: Neural Coding (NC), mimicking the neural signal transfer, and One-dimensional Convolution (OC), capturing rhythmic features. Eight parameters were trained using six datasets published in the literature to maximize Spearman’s Rank Correlation. We validated PM and six metrics of RMSE, DTW, Spectral/Temporal Matchings, ST-SIM, and SPQI in twelve datasets: six trained and six untrained datasets including measured accelerations. In all validations, PM’s predictions showed robust correlations with user data and similar structures in perceptual spaces. Other baseline metrics showed better fit in specific datasets, but none of them robustly showed correlations and similar perceptual spaces over twelve datasets.