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Chaeeun Noh

dblp:433/0862 · DBLP profile ↗
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1ranked-venue papers
1as first author
1since 2021 · last 2026
0009-0004-8479-3761ORCID · reported

Domains — the database's venue-derived domains; a paper can count in several

Human-computer interaction and ubiquitous computing · 1 · 1 first-author · 1 since 2021

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Human-computer interaction and pervasive computing
1 paper
Haptics and multimodal interaction · 77% Human-robot interaction · 23%

Topics — the 1 heaviest of 2, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Human-robot interaction › robot communication
implicit communication
0.312026
When a Robot Communicates Through Air: Contextual Interpretations of Wind and Olfactory Cues · CHI 2026

Methods — techniques the papers use, named apart from their topics

wizard-of-oz study · 1.0
YearPublicationVenuePosition
2026 When a Robot Communicates Through Air: Contextual Interpretations of Wind and Olfactory Cues
abstract
This study explores how nonverbal, sensory cues—olfactory and wind—can serve as subtle channels for behavioral guidance in mobile human–robot interaction. As multimodal interaction becomes increasingly integral to HRI, implicit communication remains underexplored, particularly through non-visual and non-auditory modalities. To address this gap, we conducted a Wizard-of-Oz study with 35 participants who experienced three types of stimuli—strong wind, weak wind, and olfactory cues—across six contextual scenarios. Our findings show that such sensory cues can induce affective interpretations ranging from support to surveillance, depending on the context. Olfactory cues generally evoked more positive impressions and a greater sense of care than wind, while wind cues were perceived as more directive and intrusive in comparison. These results suggest that scent and wind offer promising potential as ambient, affective, and non-intrusive notification channels for future human–robot interaction systems.
Chaeeun Noh, Jaejeung Kim
CHI1