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MuJie Shang

dblp:264/7463 · DBLP profile ↗
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1ranked-venue papers
0as first author
0since 2021 · last 2020
—ORCID · none

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

Human-computer interaction and ubiquitous computing · 1

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 · 44% Personal fabrication and tangible interfaces · 44% Wearable and physiological sensing · 13%

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

TopicWeightPapersLastEvidence papers
Personal fabrication and tangible interfaces › smart textiles
textile interfaces
0.412020
Fabriccio: Touchless Gestural Input on Interactive Fabrics · CHI 2020
Interaction techniques and input
touchless interaction
0.412020
Fabriccio: Touchless Gestural Input on Interactive Fabrics · CHI 2020
Wearable and physiological sensing
textile sensors
0.112020
Fabriccio: Touchless Gestural Input on Interactive Fabrics · CHI 2020

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

doppler motion sensing · 0.4conductive thread antennas · 0.4
YearPublicationVenuePosition
2020 Fabriccio: Touchless Gestural Input on Interactive Fabrics
abstract
We present Fabriccio, a touchless gesture sensing technique developed for interactive fabrics using Doppler motion sensing. Our prototype was developed using a pair of loop antennas (one for transmitting and the other for receiving), made of conductive thread that was sewn onto a fabric substrate. The antenna type, configuration, transmission lines, and operating frequency were carefully chosen to balance the complexity of the fabrication process and the sensitivity of our system for touchless hand gestures, performed at a 10 cm distance. Through a ten-participant study, we evaluated the performance of our proposed sensing technique across 11 touchless gestures as well as 1 touch gesture. The study result yielded a 92.8% cross-validation accuracy and 85.2% leave-one-session-out accuracy. We conclude by presenting several applications to demonstrate the unique interactions enabled by our technique on soft objects.
Te-Yen Wu, Shutong Qi, Junchi Chen, MuJie Shang, Jun Gong 0002, Teddy Seyed, Xing-Dong Yang
CHI4