Demo Data from VLDB 2026 / reviewers in the wild / expert
Ryosuke Kodama
dblp:24/3298
· DBLP profile ↗
1ranked-venue papers
1as first author
0since 2021 · last 2018
—ORCID · unresolved
Domains — the database's venue-derived domains; a paper can count in several
Human-computer interaction and ubiquitous computing · 1 · 1 first-author
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 |
Ubiquitous computing and smart environments · 44% Wearable and physiological sensing · 44% Immersive interaction · 13% |
Topics — the 2 heaviest of 3, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Ubiquitous computing and smart environments
context recognition |
0.3 | 1 | 2018 | A context recognition method using temperature sensors in the nostrils · UbiComp 2018 |
Immersive interaction › head-mounted display
wearable glasses |
0.1 | 1 | 2018 | A context recognition method using temperature sensors in the nostrils · UbiComp 2018 |
Methods — techniques the papers use, named apart from their topics
temperature sensing · 0.3classification · 0.3
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2018 | A context recognition method using temperature sensors in the nostrilsabstractWe can benefit from various services with context recognition using wearable sensors. In this study, we focus on the contexts acquired from sensor data in the nostrils. Nostrils can provide various contexts on breathing, nasal congestion, and higher level contexts including psychological and health states. In this paper, we propose a context recognition method using the information in the nostril. We develop a system to acquire the temperature in the nostrils using small temperature sensors connected to glasses. As a result of the evaluations, the proposed system can detect workload at an accuracy of 96.4%. Ryosuke Kodama, Tsutomu Terada, Masahiko Tsukamoto |
UbiComp | 1 |