Xianzhe Zheng

dblp:356/8170 · DBLP profile ↗
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2ranked-venue papers
0as first author
2since 2021 · last 2026
0000-0003-4731-9433ORCID · corroborated

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

Human-computer interaction and ubiquitous computing · 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
Interaction techniques and input · 56% Wearable and physiological sensing · 44%

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

TopicWeightPapersLastEvidence papers
Interaction techniques and input › input sensing › gesture recognition
facial gesture recognition
1.012026
MagFace: Interference-Resistant Facial Gesture Recognition System on Cycling Glasses with Low-Power Magnetic Sensing · CHI 2026
Wearable and physiological sensing
magnetic sensing
1.012026
MagFace: Interference-Resistant Facial Gesture Recognition System on Cycling Glasses with Low-Power Magnetic Sensing · CHI 2026
Interaction techniques and input
gesture input
0.312026
MagFace: Interference-Resistant Facial Gesture Recognition System on Cycling Glasses with Low-Power Magnetic Sensing · CHI 2026

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

machine learning · 1.0low-power magnetic sensing · 1.0
YearPublicationVenuePosition
2026 MagFace: Interference-Resistant Facial Gesture Recognition System on Cycling Glasses with Low-Power Magnetic Sensing
Guanyun Wang, Xianzhe Zheng, Huaqian Fu, Fanke Qi, Zhenxuan Ye, Ruoyu Zhai, Yinzhen Zhu, Yitao Fan, Yue Yang 0005, Qi Wang 0075, Ye Tao 0001
CHI3
2023 Dangerous Slime: A Game for Improving Situation Awareness in Automated Driving
abstract
The role of driver is changing from controller to regulator as automated vehicles become more common. This change leads to deceased situation awareness (SA) because of passive engagement and increased non-driving related tasks (NDRT), and might affected drivers' takeover. This study designed a gamified prototype named Dangerous Slime to help drivers maintain SA during automated driving. Dangerous Slime turned surrounding cars into slimes that attacked drivers' cars; drivers need to response to these attacks, which increased their attention to nearby objects. Drivers can take the game as non-driving related tasks (NDRTs) during the whole automated driving process. When compared to NDRT of watching films, the game achieved an improvement in drivers' SA and positive user feedback. Moreover, the styles of games affect drivers' behavior and SA. This study revealed how the game influenced drivers' SA, and took a step toward improving the safety of automated driving in a pleasant way.
Linhao Ye, Xianzhe Zheng, Hanfei Zhu, Wei Xiang 0008
AutomotiveUI3