Demo Data from SIGIR 2026 / reviewers in the wild / expert
Ryo Akatsu
dblp:279/2764
· 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
Computer networks · 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 |
Wearable and physiological sensing · 100% | |
| Computer networks
1 paper |
Internet of things and sensor networks · 100% |
Topics — the 2 heaviest of 3, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Wearable and physiological sensing › biosignal sensing
pulse wave sensing |
0.4 | 1 | 2020 | Real-time reassurance monitoring shopping basket in retail store: poster abstract · SenSys 2020 |
Wearable and physiological sensing › cognitive state monitoring
stress detection |
0.4 | 1 | 2020 | Real-time reassurance monitoring shopping basket in retail store: poster abstract · SenSys 2020 |
Methods — techniques the papers use, named apart from their topics
pulse wave measurement · 0.9RR interval estimation · 0.9
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2020 | Real-time reassurance monitoring shopping basket in retail store: poster abstractabstractIn this study, we propose a sensor network to estimate service reassurance in a store by estimating customer stress level based on a pulse wave. We developed a basket that can calculate an RR interval (RRI) with sufficient accuracy to estimate customer reassurance from the measured pulse wave under in-store conditions. Two processes were applied to the measurement values based on the assumption that certain circumstances may interfere with measuring pulse waves. Furthermore, the authors conducted pulse wave measurement experiments under simulated store conditions. The results showed RRIs' values with an error rate of 4.9% compared to the true value. Azusa Yamazaki, Ryo Akatsu, Yukihiko Okada, Keiichi Zempo |
SenSys | 2 |