Demo Data from VLDB 2026 / reviewers in the wild / expert
Vimal Chandran
dblp:93/8663 · also C. Vimal, Vimal Kumar Chandran
· DBLP profile ↗
3ranked-venue papers
2as first author
0since 2021 · last 2019
0000-0002-2331-776XORCID · verified
Domains — the database's venue-derived domains; a paper can count in several
Applied, interdisciplinary, general and emerging computing · 2 · 2 first-authorGraphics, computer vision, multimedia, augmented reality and games · 1 · 1 first-authorHuman-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
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Learning and educational technologies › embodied learning
haptic learning |
0.2 | 1 | 2016 | Haptic Learning of Semaphoric Finger Gestures · UIST 2016 |
Haptics and multimodal interaction
wearable haptic device |
0.2 | 1 | 2016 | Haptic Learning of Semaphoric Finger Gestures · UIST 2016 |
Interaction techniques and input
gesture input |
0.1 | 1 | 2016 | 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
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2019 | Supervised learning for bone shape and cortical thickness estimation from CT images for finite element analysis
Vimal Chandran, Ghislain Maquer, Thomas Gerig, Philippe Zysset, Mauricio Reyes 0001 |
Medical Image Anal. | 1 |
| 2016 | Haptic Learning of Semaphoric Finger GesturesabstractHaptic 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 |
UIST | 3 |
| 2015 | Prediction of Trabecular Bone Anisotropy from Quantitative Computed Tomography Using Supervised Learning and a Novel Morphometric Feature Descriptor
Vimal Chandran, Philippe Zysset, Mauricio Reyes 0001 |
MICCAI (1) | 1 |