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Kaiyi Zou

dblp:366/2738 · DBLP profile ↗
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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

TopicWeightPapersLastEvidence papers
Wearable and physiological sensing › motion sensing
motion tracking
0.812024
UltraMotion: High-Precision Ultrasonic Arm Tracking for Real-World Exercises · IEEE Trans. Mob. Comput. 2024
Wireless sensing and localization › ranging
acoustic ranging
0.812024
UltraMotion: High-Precision Ultrasonic Arm Tracking for Real-World Exercises · IEEE Trans. Mob. Comput. 2024
Health and well-being technologies › physical activity
exercise monitoring
0.212024
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
YearPublicationVenuePosition
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 Exercises
abstract
Home 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