EDBT 2026 Demo / reviewers in the wild / expert
Junhan Zhou
dblp:179/4791
· DBLP profile ↗
2ranked-venue papers
1as first author
0since 2021 · last 2016
0000-0001-8902-7514ORCID · corroborated
Domains — the database's venue-derived domains; a paper can count in several
Human-computer interaction and ubiquitous computing · 2 · 1 first-author
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
2 papers |
Interaction techniques and input · 100% |
Topics — the 6 heaviest of 7, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Interaction techniques and input › spatial interaction
around-device interaction |
0.2 | 1 | 2016 | AuraSense: Enabling Expressive Around-Smartwatch Interactions with Electric Field Sensing · UIST 2016 |
Interaction techniques and input › input sensing › touch sensing
electric field sensing |
0.2 | 1 | 2016 | AuraSense: Enabling Expressive Around-Smartwatch Interactions with Electric Field Sensing · UIST 2016 |
Interaction techniques and input › input sensing › tracking › hand tracking
finger tracking |
0.2 | 1 | 2016 | SkinTrack: Using the Body as an Electrical Waveguide for Continuous Finger Tracking on the Skin · CHI 2016 |
Interaction techniques and input › touch interaction
on-skin interaction |
0.2 | 1 | 2016 | SkinTrack: Using the Body as an Electrical Waveguide for Continuous Finger Tracking on the Skin · CHI 2016 |
Interaction techniques and input › touch interaction
touch input |
0.2 | 1 | 2016 | SkinTrack: Using the Body as an Electrical Waveguide for Continuous Finger Tracking on the Skin · CHI 2016 |
Interaction techniques and input › mobile interaction › wearable device interaction
smartwatch interaction |
0.1 | 1 | 2016 | AuraSense: Enabling Expressive Around-Smartwatch Interactions with Electric Field Sensing · UIST 2016 |
Methods — techniques the papers use, named apart from their topics
phase difference measurement · 0.2feasibility quantification · 0.2electrical waveguide sensing · 0.2antenna configuration study · 0.2
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2016 | SkinTrack: Using the Body as an Electrical Waveguide for Continuous Finger Tracking on the SkinabstractSkinTrack is a wearable system that enables continuous touch tracking on the skin. It consists of a ring, which emits a continuous high frequency AC signal, and a sensing wristband with multiple electrodes. Due to the phase delay inherent in a high-frequency AC signal propagating through the body, a phase difference can be observed between pairs of electrodes. SkinTrack measures these phase differences to compute a 2D finger touch coordinate. Our approach can segment touch events at 99% accuracy, and resolve the 2D location of touches with a mean error of 7.6mm. As our approach is compact, non-invasive, low-cost and low-powered, we envision the technology being integrated into future smartwatches, supporting rich touch interactions beyond the confines of the small touchscreen. Yang Zhang 0041, Junhan Zhou, Gierad Laput, Chris Harrison 0001 |
CHI | 2 |
| 2016 | AuraSense: Enabling Expressive Around-Smartwatch Interactions with Electric Field SensingabstractExisting smartwatches rely on touchscreens for display and input, which inevitably leads to finger occlusion and confines interactivity to a small area. In this work, we introduce AuraSense, which enables rich, around-device, smartwatch interactions using electric field sensing as an adapted device. To explore how this sensing approach could enhance smartwatch interactions, we considered different antenna configurations and how they could enable useful interaction modalities. We identified four configurations that can support six well-known modalities of particular interest and utility, including gestures above or in close proximity to watches, and touchscreen-like finger tracking on the skin. We quantify the feasibility of these input modalities, suggesting that AuraSense can be low latency and robust across users and environments. Junhan Zhou, Yang Zhang 0041, Gierad Laput, Chris Harrison 0001 |
UIST | 1 |