Demo Data from EDBT 2026 / reviewers in the wild / expert
Kato Akira
dblp:433/3101
· DBLP profile ↗
1ranked-venue papers
0as first author
1since 2021 · last 2026
0000-0002-6129-7747ORCID · reported
Domains — the database's venue-derived domains; a paper can count in several
Human-computer interaction and ubiquitous computing · 1 · 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 · 44% Health and well-being technologies · 44% Haptics and multimodal interaction · 13% |
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
electromyography |
1.0 | 1 | 2026 | Sensing Your Vocals: Exploring the Activity of Vocal Cord Muscles for Pitch Assessment Using Electromyography and Ultrasonography · CHI 2026 |
Health and well-being technologies
vocal training |
1.0 | 1 | 2026 | Sensing Your Vocals: Exploring the Activity of Vocal Cord Muscles for Pitch Assessment Using Electromyography and Ultrasonography · CHI 2026 |
Methods — techniques the papers use, named apart from their topics
ultrasonography · 1.0electromyography · 1.0
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2026 | Sensing Your Vocals: Exploring the Activity of Vocal Cord Muscles for Pitch Assessment Using Electromyography and UltrasonographyabstractVocal training is difficult because the muscles that control pitch, resonance, and phonation are internal and invisible to learners. This paper investigates how Electromyography (EMG) and ultrasonic imaging (UI) can make these muscles observable for training purposes. We report three studies. First, we analyze the EMG and UI data from 16 singers (beginners, experienced & professionals), revealing differences among three vocal groups of the muscle control proficiency. Second, we use the collected data to create a system that visualizes an expert’s muscle activity as reference. This system is tested in a user study with 12 novices, showing that EMG highlighted muscle activation nuances, while UI provided insights into vocal cord length and dynamics. Third, to compare our approach to traditional methods (audio analysis and coach instructions), we conducted a focus group study with 15 experienced singers. Our results suggest that EMG is promising for improving vocal skill development and enhancing feedback systems. We conclude the paper with a detailed comparison of the analyzed modalities (EMG, UI and traditional methods), resulting in recommendations to improve vocal muscle training systems. Kanyu Chen, Rebecca Panskus, Erwin Wu, Yichen Peng, Daichi Saito, Emiko Kamiyama, Ruiteng Li, Chen-Chieh Liao, Karola Marky, Kato Akira, Hideki Koike, Kai Kunze |
CHI | 10 |