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Hsiao-Chuan Huang

dblp:336/4317 · DBLP profile ↗
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1ranked-venue papers
0as first author
1since 2021 · last 2022
—ORCID · none

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

Graphics, computer vision, multimedia, augmented reality and games · 1 · 1 since 2021Human-computer interaction and ubiquitous computing · 1 · 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% Immersive interaction · 33%

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

TopicWeightPapersLastEvidence papers
Interaction techniques and input › target selection › pointing
fitts' law
0.612022
Comparing the Fidelity of Contemporary Pointing with Controller Interactions on Performance of Personal Space Target Selection · ISMAR 2022
Interaction techniques and input
pointing and selection
0.612022
Comparing the Fidelity of Contemporary Pointing with Controller Interactions on Performance of Personal Space Target Selection · ISMAR 2022
Immersive interaction
virtual reality interaction
0.612022
Comparing the Fidelity of Contemporary Pointing with Controller Interactions on Performance of Personal Space Target Selection · ISMAR 2022

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

user study · 0.6fitts' law modeling · 0.6
YearPublicationVenuePosition
2022 Comparing the Fidelity of Contemporary Pointing with Controller Interactions on Performance of Personal Space Target Selection
abstract
The goal of this research is to provide much needed empirical data on how the fidelity of popular hand gesture tracked based pointing metaphors versus commodity controller based input affects the efficiency and speed-accuracy tradeoff in users’ spatial selection in personal space interactions in VR. We conduct two experiments in which participants select spherical targets arranged in a circle in personal space, or near-field within their maximum arms reach distance, in VR. Both experiments required participants to select the targets with either a VR controller or with their dominant hand’s index finger, which was tracked with one of two popular contemporary tracking methods. In the first experiment, the targets are arranged in a flat circle in accordance with the ISO 9241-9 Fitts’ law standard, and the simulation selected random combinations of 3 target amplitudes and 3 target widths. Targets were placed centered around the users’ eye level, and the arrangement was placed at either 60%, 75%, or 90% depth plane of the users’ maximum arm’s reach. In experiment 2, the targets varied in depth randomly from one depth plane to another within the same configuration of 13 targets within a trial set, which resembled button selection task in hierarchical menus in differing depth planes in the near-field. The study was conducted using the HTC Vive head-mounted display, and used either a VR controller (HTC Vive), low-fidelity virtual pointing (Leap Motion), or a high-fidelity virtual pointing (tracked VR glove) conditions. Our results revealed that low-fidelity pointing performed worse than both high-fidelity pointing and the VR controller. Overall, target selection performance was found to be worse in depth planes closer to the maximum arms reach, as compared to middle and nearer distances.
Sabarish V. Babu, Hsiao-Chuan Huang, Robert J. Teather, Jung-Hong Chuang
ISMAR2