Demo Data from SIGIR 2026 / reviewers in the wild / expert
Siri Avula
dblp:322/7128
· DBLP profile ↗
1ranked-venue papers
0as first author
1since 2021 · last 2022
—ORCID · none
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 |
Health and well-being technologies · 100% |
Topics — the 2 heaviest of 2, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Wireless sensing and localization
sleep monitoring |
0.6 | 1 | 2022 | mmSleep: monitoring sleep posture from commodity millimeter-wave devices · MobiSys 2022 |
Health and well-being technologies
sleep health |
0.2 | 1 | 2022 | mmSleep: monitoring sleep posture from commodity millimeter-wave devices · MobiSys 2022 |
Methods — techniques the papers use, named apart from their topics
convolutional neural network · 1.13d joint localization · 1.1
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2022 | mmSleep: monitoring sleep posture from commodity millimeter-wave devicesabstractWe propose mmSleep, a millimeter-wave (mmWave) wireless signal based sleep posture monitoring system that can assist in tracking 3D location of body joints of a person during sleep. mmSleep overcomes the limitations of existing vision-based sleep monitoring and can work under low-light without being privacy-invasive. mmSleep uses a customized Convolutional Neural Network to learn diverse sleep postures, and our preliminary results show that mmSleep can consistently predict 3D joint locations with high accuracy. Aakriti Adhikari, Siri Avula, Sanjib Sur 0001 |
MobiSys | 2 |