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Freddy Yifei Liu

dblp:420/3204 · DBLP profile ↗
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1ranked-venue papers
0as first author
1since 2021 · last 2026
0009-0004-9992-7339ORCID · reported

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

Computer networks · 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.

Computer networks
1 paper
Wireless sensing and localization · 100%
Human-computer interaction and pervasive computing
1 paper
Wearable and physiological sensing · 100%

Topics — the 4 heaviest of 5, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Wireless sensing and localization › device-free sensing
device-free localization
1.012026
Motion Capture with Millimeter-Wave Tags · SenSys 2026
Wireless sensing and localization
mmwave sensing
1.012026
Motion Capture with Millimeter-Wave Tags · SenSys 2026
Wireless sensing and localization › human sensing › human activity sensing
motion capture
1.012026
Motion Capture with Millimeter-Wave Tags · SenSys 2026
Wearable and physiological sensing › motion sensing
motion tracking
0.312026
Motion Capture with Millimeter-Wave Tags · SenSys 2026

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

phase-based tracking · 2.0backscatter communication · 2.0
YearPublicationVenuePosition
2026 Motion Capture with Millimeter-Wave Tags
abstract
This paper introduces M3oCap, a millimeter-wave (mmWave) tag-based motion capture system that delivers accurate 6 degrees of freedom motion tracking. M3oCap utilizes a single commercial mmWave radar and custom-designed mmWave backscatter tags (2.5 cm × 3 cm) to localize and track the motion of tagged objects. Our system features novel algorithms that effectively isolate weak backscattered signals while accurately recovering phase changes induced by motion, allowing high-rate tracking of tag movements. Experiments show that M3oCap achieves performance close to commercial motion capture systems – it provides 1000 measurements per second with a median range tracking accuracy of 250 μ m, median localization accuracies of 3.34 mm, 3.95 mm, and 4.20 mm in x, y, z dimensions, and median orientation tracking accuracies of 1.7°, 2.3°, and 2.0° across pitch, roll, and yaw. We further demonstrate the system’s fine-grained movement tracking capabilities through various motion capture tasks.
Freddy Yifei Liu, Yunshuang Li, Yao Gong, Dinesh Jayaraman, Omid Abari, Mingmin Zhao
SenSys2