VLDB 2026 Research / reviewers in the wild / expert
Kaiyi Zou
dblp:366/2738
· DBLP profile ↗
2ranked-venue papers
0as first author
2since 2021 · last 2024
0000-0002-0708-1718ORCID · reported
Domains — the database's venue-derived domains; a paper can count in several
Computer networks · 2 · 2 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 · 77% Health and well-being technologies · 23% | |
| Computer networks
1 paper |
Wireless sensing and localization · 100% |
Topics — the 3 heaviest of 3, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Wearable and physiological sensing › motion sensing
motion tracking |
0.8 | 1 | 2024 | UltraMotion: High-Precision Ultrasonic Arm Tracking for Real-World Exercises · IEEE Trans. Mob. Comput. 2024 |
Wireless sensing and localization › ranging
acoustic ranging |
0.8 | 1 | 2024 | UltraMotion: High-Precision Ultrasonic Arm Tracking for Real-World Exercises · IEEE Trans. Mob. Comput. 2024 |
Health and well-being technologies › physical activity
exercise monitoring |
0.2 | 1 | 2024 | UltraMotion: High-Precision Ultrasonic Arm Tracking for Real-World Exercises · IEEE Trans. Mob. Comput. 2024 |
Methods — techniques the papers use, named apart from their topics
point cloud query · 1.5hidden markov model · 1.5extended kalman filter · 1.5
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2024 | Smartphone Indoor Fusion Localization with Trust Region-Based Magnetic Matching
Kaiyi Zou, Xiaoguang Niu |
WASA (1) | 3 |
| 2024 | UltraMotion: High-Precision Ultrasonic Arm Tracking for Real-World ExercisesabstractHome exercise and self-served gyms allow a larger population to exercise regularly without the cost of hiring private coaches. In absence of professional guidance, however, exercisers can suffer from injuries to muscles and joints. High-precision, affordable arm tracking with commercial, off-the-shelf (COTS) wearable devices has become an urgent need to prevent workout injuries and improve exercise performance. Recent studies with inertial measurement units (IMUs) or audio signals are neither computationally feasible for real-time motion tracking with satisfactory accuracy using COTS devices nor practically usable due to the interference with noisy ambient environments. In this paper, we propose UltraMotion, a real-time, high-precision ultrasonic arm motion tracking system designed for practical use. UltraMotion performs point cloud queries based on hidden Markov models (HMMs), a novel ultrasonic acoustic ranging method, and an extended Kalman filter (EKF) to predict the locations of all three arm joints, making it the first system offering shoulder locations. Experimental results with only a smartphone and a smartwatch demonstrate the effectiveness of UltraMotion in tracking shoulder, elbow, and wrist locations with impressively small median errors of 6.4 cm, 7.1 cm, and 8.5 cm in real-world environments, outperforming all previous systems, making UltraMotion an ideal choice for daily exercise. Xiaoguang Niu, Kaiyi Zou, Da Shen, He Zhu 0002, Shaowu Wu, Guangyi Guo, Ruizhi Chen |
IEEE Trans. Mob. Comput. | 2 |