Demonstration venue · read-only. Every page can be browsed; the buttons that would change it are switched off. Create an account to run TaxoReview on your own data. A simulation from public publication records: the people listed were not invited and did not agree to review.
? Tutorial for this page
Demo Data from EDBT 2026 / reviewers in the wild / expert

Ryotaro Kageshima

dblp:377/0708 · DBLP profile ↗
← Back
1ranked-venue papers
1as first author
1since 2021 · last 2024
0009-0006-8771-9019ORCID · reported

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

Computer networks · 1 · 1 first-author · 1 since 2021

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%
Artificial intelligence
1 paper
Face, body and person analysis · 100%

Topics — the 2 heaviest of 2, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Wearable and physiological sensing
facial expression analysis		 0.812024
Poster: Customer satisfaction estimation using facial expression analysis · MobiSys 2024
Computer vision › Face, body and person analysis
facial action unit analysis		 0.212024
Poster: Customer satisfaction estimation using facial expression analysis · MobiSys 2024

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

openface · 1.5gradient boosting · 1.5
YearPublicationVenuePosition
2024 Poster: Customer satisfaction estimation using facial expression analysis
abstract
This study presents SatisFace, a novel approach to measuring customer satisfaction in amusement parks by analyzing facial expressions at entry and exit points. Recognizing satisfaction's subjectivity, SatisFace assesses facial Action Unit (AU) changes, tailoring assessments to individual user characteristics. Data were collected through an experiment with 200 participants at Soleil Hill, using questionnaires and OpenFace for facial analysis. A gradient-boosting machine learning model revealed a significant correlation between changes in facial expressions and satisfaction levels.
Ryotaro Kageshima, Satoki Hamanaka, Shuri Marui, Akira Tsuge, Jin Nakazawa, Tadashi Okoshi
 MobiSys1