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Shumin Zhai
Researcher at Google
Publications - 204
Citations - 14225
Shumin Zhai is an academic researcher from Google. The author has contributed to research in topics: Input device & Gesture. The author has an hindex of 67, co-authored 200 publications receiving 13447 citations. Previous affiliations of Shumin Zhai include Nuance Communications & IBM.
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Proceedings ArticleDOI
Manual and gaze input cascaded (MAGIC) pointing
TL;DR: This work explores a new direction in utilizing eye gaze for computer input by proposing an alternative approach, dubbed MAGIC (Manual And Gaze Input Cascaded) pointing, which might offer many advantages, including reduced physical effort and fatigue as compared to traditional manual pointing, greater accuracy and naturalness than traditional gaze pointing, and possibly fasterspeed than manual pointing.
Proceedings ArticleDOI
Beyond Fitts' law: models for trajectory-based HCI tasks
Johnny Accot,Shumin Zhai +1 more
TL;DR: A great number of studies have verified and / or applied Fitts' law to HCI problems, making Fitt's' law one of the most intensively studied topic in the HCI literature.
Proceedings ArticleDOI
SHARK2: a large vocabulary shorthand writing system for pen-based computers
Per Ola Kristensson,Shumin Zhai +1 more
TL;DR: The architecture, algorithms and interfaces of a high-capacity multi-channel pen-gesture recognition system that supports a gradual and seamless transition from visually guided tracing to recall-based gesturing are designed and implemented.
Proceedings ArticleDOI
High precision touch screen interaction
Pär-Anders Albinsson,Shumin Zhai +1 more
TL;DR: Two techniques are proposed: Cross-Keys that uses discrete taps on virtual keys integrated with a crosshair cursor, and an analog Precision-Handle that uses a leverage (gain) effect to amplify movement precision from the user's finger tip to the end cursor.
Proceedings ArticleDOI
More than dotting the i's --- foundations for crossing-based interfaces
Johnny Accot,Shumin Zhai +1 more
TL;DR: This paper systematically evaluates two target-pointing tasks and four goal-crossing tasks, which differ by the direction of the movement variability constraint (collinear vs. orthogonal) and by the nature of the action (pointing vs. crossing, discrete vs. continuous).