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Alexander Kyu

dblp:374/8801 · DBLP profile ↗
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1ranked-venue papers
1as first author
1since 2021 · last 2024
0009-0004-6207-6024ORCID · reported

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

Human-computer interaction and ubiquitous computing · 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
Interaction techniques and input · 67% Wearable and physiological sensing · 33%

Topics — the 3 heaviest of 4, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Wearable and physiological sensing
electrical impedance tomography
0.812024
EITPose: Wearable and Practical Electrical Impedance Tomography for Continuous Hand Pose Estimation · CHI 2024
Interaction techniques and input
gesture input
0.812024
EITPose: Wearable and Practical Electrical Impedance Tomography for Continuous Hand Pose Estimation · CHI 2024
Interaction techniques and input › input sensing › tracking › hand tracking
hand pose estimation
0.812024
EITPose: Wearable and Practical Electrical Impedance Tomography for Continuous Hand Pose Estimation · CHI 2024

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

electrical impedance tomography · 0.8
YearPublicationVenuePosition
2024 EITPose: Wearable and Practical Electrical Impedance Tomography for Continuous Hand Pose Estimation
abstract
Real-time hand pose estimation has a wide range of applications spanning gaming, robotics, and human-computer interaction. In this paper, we introduce EITPose, a wrist-worn, continuous 3D hand pose estimation approach that uses eight electrodes positioned around the forearm to model its interior impedance distribution during pose articulation. Unlike wrist-worn systems relying on cameras, EITPose has a slim profile (12 mm thick sensing strap) and is power-efficient (consuming only 0.3 W of power), making it an excellent candidate for integration into consumer electronic devices. In a user study involving 22 participants, EITPose achieves with a within-session mean per joint positional error of 11.06 mm. Its camera-free design prioritizes user privacy, yet it maintains cross-session and cross-user accuracy levels comparable to camera-based wrist-worn systems, thus making EITPose a promising technology for practical hand pose estimation.
Alexander Kyu, Hongyu Mao, Junyi Zhu 0001, Mayank Goel, Karan Ahuja
CHI1