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Goutham Palaniappan

dblp:187/9607 · DBLP profile ↗
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1ranked-venue papers
0as first author
0since 2021 · last 2016
—ORCID · none

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

Human-computer interaction and ubiquitous computing · 1

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
Learning and educational technologies · 44% Haptics and multimodal interaction · 44% Interaction techniques and input · 13%

Topics — the 3 heaviest of 3, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Learning and educational technologies › embodied learning
haptic learning		 0.212016
Haptic Learning of Semaphoric Finger Gestures · UIST 2016
Haptics and multimodal interaction
wearable haptic device		 0.212016
Haptic Learning of Semaphoric Finger Gestures · UIST 2016
Interaction techniques and input
gesture input		 0.112016
Haptic Learning of Semaphoric Finger Gestures · UIST 2016

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

two-day user study · 0.2multimodal stimuli coupling · 0.2
YearPublicationVenuePosition
2016 Haptic Learning of Semaphoric Finger Gestures
abstract
Haptic learning of gesture shortcuts has never been explored. In this paper, we investigate haptic learning of a freehand semaphoric finger tap gesture shortcut set using haptic rings. We conduct a two-day study of 30 participants where we couple haptic stimuli with visual and audio stimuli, and compare their learning performance with wholly visual learning. The results indicate that with <30 minutes of learning, haptic learning of finger tap semaphoric gestures is comparable to visual learning and maintains its recall on the second day.
Aakar Gupta, Antony Irudayaraj, Vimal Chandran, Goutham Palaniappan, Khai N. Truong, Ravin Balakrishnan
 UIST4