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Emotional and sensory ratings of vibration Tactons in the lab and crowdsourced settings

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Abstract
Vibrotactile actuators in consumer devices present new opportunities to crowdsource subjective ratings of tactile icons (i.e., Tactons). Yet, little is known about the effects of user demographics, hardware form factors, and study environments on subjective ratings. Also, the feasibility of crowdsourcing emotional and sensory ratings remains underexplored. To address this gap, we investigated valence, arousal, and roughness ratings by controlling for the Tacton design approach, biological sex, form factor, and study environment. Study 1 investigated the effects of two biological sexes and three smartphone models on the ratings from 36 participants in a controlled laboratory setting using two sets of 24 Tactons (48 in total) created using different design approaches. Strong correlations (mean Pearson's r=0.86) existed in all ratings across biological sexes regardless of Tacton sets, while valence ratings showed moderate or low correspondence across smartphone models depending on Tacton set. In Study 2, we crowdsourced the Tacton ratings with 36 new participants to explore the impact of an uncontrolled setting on the results. We identified a subset of parameters, such as duration, that were influenced by the settings, where demographics and form factors varied. Also, arousal and roughness ratings demonstrated strong correspondences with 85% and 77% of statistically equivalent Tactons between the lab and crowdsourced settings, but valence ratings showed moderate to strong correlations depending on Tacton design approaches. Based on these findings, we offer guidelines for Tacton design in uncontrolled settings and for crowdsourcing emotional and sensory ratings. © 2025 Elsevier Ltd
Author(s)
Lim, ChungmanKim, GyeongdeokShetty, YatirajMcDaniel, TroySeifi, HastiPark, Gunhyuk
Issued Date
2025-03
Type
Article
DOI
10.1016/j.ijhcs.2025.103446
URI
https://scholar.gist.ac.kr/handle/local/9009
Publisher
Academic Press
Citation
International Journal of Human Computer Studies, v.197
ISSN
1071-5819
Appears in Collections:
Department of AI Convergence > 1. Journal Articles
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