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Zimo Liao

dblp:323/2534 · DBLP profile ↗
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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

TopicWeightPapersLastEvidence papers
Interaction techniques and input
gesture input
1.022021
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
1.022021
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
0.812024
Gesture Recognition Using Visible Light on Mobile Devices · IEEE/ACM Trans. Netw. 2024
Interaction techniques and input › gesture input
mid-air gestures
0.812024
Gesture Recognition Using Visible Light on Mobile Devices · IEEE/ACM Trans. Netw. 2024
Interaction techniques and input
mobile interaction
0.812024
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
YearPublicationVenuePosition
2024 Gesture Recognition Using Visible Light on Mobile Devices
abstract
In-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 devices
abstract
In-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
MobiCom1
2021 SMART: screen-based gesture recognition on commodity mobile devices
abstract
In-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
MobiCom1