Demo Data from EDBT 2026 / reviewers in the wild / expert
Yinshuai Zhang
dblp:222/6632
· DBLP profile ↗
1ranked-venue papers
0as first author
0since 2021 · last 2020
—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 |
Interaction techniques and input · 50% Wearable and physiological sensing · 50% |
Topics — the 2 heaviest of 2, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Interaction techniques and input
gesture input |
0.4 | 1 | 2020 | ThermalRing: Gesture and Tag Inputs Enabled by a Thermal Imaging Smart Ring · CHI 2020 |
Wearable and physiological sensing › smart wearable
smart ring |
0.4 | 1 | 2020 | ThermalRing: Gesture and Tag Inputs Enabled by a Thermal Imaging Smart Ring · CHI 2020 |
Methods — techniques the papers use, named apart from their topics
thermal imaging · 0.4gesture recognition · 0.4
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2020 | ThermalRing: Gesture and Tag Inputs Enabled by a Thermal Imaging Smart RingabstractThe heterogeneous and ubiquitous input demands in smart spaces call for an input device that can enable rich and spontaneous interactions. We propose ThermalRing, a thermal imaging smart ring using low-resolution thermal camera for identity-anonymous, illumination-invariant, and power-efficient sensing of both dynamic and static gestures. We also design ThermalTag, thin and passive thermal imageable tags that reflect the heat from the human hand. ThermalTag can be easily made and applied onto everyday objects by users. We develop sensing techniques for three typical input demands: drawing gestures for device pairing, click and slide gestures for device control, and tag scan gestures for quick access. The study results show that ThermalRing can recognize nine drawing gestures with an overall accuracy of 90.9%, detect click gestures with an accuracy of 94.9%, and identify among six ThermalTags with an overall accuracy of 95.0%. Finally, we show the versatility and potential of ThermalRing through various applications. Tengxiang Zhang, Yinshuai Zhang, Ke Sun 0003, Yuntao Wang 0001, Yiqiang Chen 0001 |
CHI | 3 |