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Feichi Huang

dblp:344/8651 · DBLP profile ↗
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1ranked-venue papers
0as first author
1since 2021 · last 2023
0000-0003-0575-463XORCID · reported

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

Human-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
Learning and educational technologies · 62% User interface design and tools · 19% Haptics and multimodal interaction · 19%

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

TopicWeightPapersLastEvidence papers
Learning and educational technologies › educational content generation
tutorial generation
0.712023
InstruMentAR: Auto-Generation of Augmented Reality Tutorials for Operating Digital Instruments Through Recording Embodied Demonstration · CHI 2023
User interface design and tools › end-user programming
demonstrational interfaces
0.212023
InstruMentAR: Auto-Generation of Augmented Reality Tutorials for Operating Digital Instruments Through Recording Embodied Demonstration · CHI 2023
Haptics and multimodal interaction
haptic feedback
0.212023
InstruMentAR: Auto-Generation of Augmented Reality Tutorials for Operating Digital Instruments Through Recording Embodied Demonstration · CHI 2023

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

voice recognition · 0.7pressure sensing · 0.7gesture recognition · 0.7
YearPublicationVenuePosition
2023 InstruMentAR: Auto-Generation of Augmented Reality Tutorials for Operating Digital Instruments Through Recording Embodied Demonstration
abstract
Augmented Reality tutorials, which provide necessary context by directly superimposing visual guidance on the physical referent, represent an effective way of scaffolding complex instrument operations. However, current AR tutorial authoring processes are not seamless as they require users to continuously alternate between operating instruments and interacting with virtual elements. We present InstruMentAR, a system that automatically generates AR tutorials through recording user demonstrations. We design a multimodal approach that fuses gestural information and hand-worn pressure sensor data to detect and register the user’s step-by-step manipulations on the control panel. With this information, the system autonomously generates virtual cues with designated scales to respective locations for each step. Voice recognition and background capture are employed to automate the creation of text and images as AR content. For novice users receiving the authored AR tutorials, we facilitate immediate feedback through haptic modules. We compared InstruMentAR with traditional systems in the user study.
Ziyi Liu 0004, Zhengzhe Zhu, Enze Jiang, Feichi Huang, Ana M. Villanueva, Xun Qian, Tianyi Wang 0004, Karthik Ramani
CHI4