VLDB 2026 Research / reviewers in the wild / expert
Zimo Liao
dblp:323/2534
· DBLP profile ↗
3ranked-venue papers
3as first author
3since 2021 · last 2024
0009-0002-5749-9183ORCID · corroborated
Domains — the database's venue-derived domains; a paper can count in several
Computer networks · 3 · 3 first-author · 3 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
3 papers |
Interaction techniques and input · 89% Ubiquitous computing and smart environments · 6% Wearable and physiological sensing · 5% |
Topics — the 5 heaviest of 8, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Interaction techniques and input
gesture input |
1.0 | 2 | 2021 | SMART: screen-based gesture recognition on commodity mobile devices · MobiCom 2021 SMART: screen-based gesture recognition on commodity mobile devices · MobiCom 2021 |
Interaction techniques and input › input sensing › gesture recognition
in-air gesture recognition |
1.0 | 2 | 2021 | SMART: screen-based gesture recognition on commodity mobile devices · MobiCom 2021 SMART: screen-based gesture recognition on commodity mobile devices · MobiCom 2021 |
Interaction techniques and input › input sensing
gesture recognition |
0.8 | 1 | 2024 | Gesture Recognition Using Visible Light on Mobile Devices · IEEE/ACM Trans. Netw. 2024 |
Interaction techniques and input › gesture input
mid-air gestures |
0.8 | 1 | 2024 | Gesture Recognition Using Visible Light on Mobile Devices · IEEE/ACM Trans. Netw. 2024 |
Interaction techniques and input
mobile interaction |
0.8 | 1 | 2024 | Gesture Recognition Using Visible Light on Mobile Devices · IEEE/ACM Trans. Netw. 2024 |
Methods — techniques the papers use, named apart from their topics
screen-based sensing · 1.0signal processing · 0.8machine learning · 0.8
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2024 | Gesture Recognition Using Visible Light on Mobile DevicesabstractIn-air gesture control extends a touch screen and enables contactless interaction, thus has become a popular research direction in the past few years. Prior work has implemented this functionality based on cameras, acoustic signals, and Wi-Fi via existing hardware on commercial devices. However, these methods have low user acceptance. Solutions based on cameras and acoustic signals raise privacy concerns, while WiFi-based solutions are vulnerable to background noise. As a result, these methods are not commercialized and recent flagship smartphones have implemented in-air gesture recognition by adding extra hardware on-board, such as mmWave radar and depth camera. The question is, can we support in-air gesture control on legacy devices without any hardware modifications? To answer this question, in this work, we propose, an in-air gesture recognition system leveraging the screen and ambient light sensor (ALS), which are ordinary modalities on mobile devices. For the transmitter side, we design a screen display mechanism to embed spatial information and preserve the viewing experience; for the receiver side, we develop a framework to recognize gestures from low-quality ALS readings. We implement and evaluate on both a tablet and several smartphones. Results show that can recognize$9$types of frequently used in-air gestures with an average accuracy of$96.1\%$. Zimo Liao, Zhicheng Luo, Qianyi Huang, Linfeng Zhang 0001, Fan Wu 0006, Qian Zhang 0001, Guihai Chen |
IEEE/ACM Trans. Netw. | 1 |
| 2021 | SMART: screen-based gesture recognition on commodity mobile devicesabstractIn-air gesture control extends a touch screen and enables contactless interaction, thus has become a popular research direction in the past few years. Prior work has implemented this functionality based on cameras, acoustic signals, and Wi-Fi via existing hardware on commercial devices. However, these methods have low user acceptance. Solutions based on cameras and acoustic signals raise privacy concerns, while WiFi-based solutions are vulnerable to background noise. As a result, these methods are not commercialized and recent flagship smartphones have implemented in-air gesture recognition by adding extra hardware on-board, such as mmWave radar and depth camera. The question is, can we support in-air gesture control on legacy devices without any hardware modifications? Zimo Liao, Zhicheng Luo, Qianyi Huang, Linfeng Zhang 0001, Fan Wu 0006, Qian Zhang 0001, Yi Wang 0004, Guihai Chen |
MobiCom | 1 |
| 2021 | SMART: screen-based gesture recognition on commodity mobile devicesabstractIn-air gesture control extends a touch screen and enables contact-less interaction, thus has become a popular research direction in the past few years. Prior work has implemented this functionality based on cameras, acoustic signals, and Wi-Fi via existing hardware on commercial devices. However, these methods have low user acceptance. Solutions based on cameras and acoustic signals raise privacy concerns, while WiFi-based solutions are vulnerable to background noise. As a result, these methods are not commercialized and recent flagship smartphones have implemented in-air gesture recognition by adding extra hardware on-board, such as mmWave radar and depth camera. The question is, can we support in-air gesture control on legacy devices without any hardware modifications? Zimo Liao, Zhicheng Luo, Qianyi Huang, Linfeng Zhang 0001, Fan Wu 0006, Qian Zhang 0001, Yi Wang 0004 |
MobiCom | 1 |