Demo Data from SIGIR 2026 / reviewers in the wild / expert
Seyedeh Zohreh Homayounfar
dblp:251/4424
· DBLP profile ↗
2ranked-venue papers
0as first author
2since 2021 · last 2022
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Computer networks · 1 · 1 since 2021Applied, interdisciplinary, general and emerging computing · 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.
| Human-computer interaction and pervasive computing
1 paper |
Wearable and physiological sensing · 62% Interaction techniques and input · 19% Games and playful interaction · 19% |
Topics — the 1 heaviest of 3, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Wearable and physiological sensing › textile sensors
textile pressure sensing |
0.6 | 1 | 2022 | FabToys: plush toys with large arrays of fabric-based pressure sensors to enable fine-grained interaction detection · MobiSys 2022 |
Methods — techniques the papers use, named apart from their topics
fabric-based sensors · 0.6
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2022 | FabToys: plush toys with large arrays of fabric-based pressure sensors to enable fine-grained interaction detectionabstractRecent advances in fabric-based sensors have made it possible to densely instrument textile surfaces on smart toys without changing their look and feel. While such surfaces can be instrumented with traditional sensors, rigid elements change the nature of interaction and diminish the appeal of plush toys. Ali Kiaghadi, Seyedeh Zohreh Homayounfar, Trisha Andrew, Deepak Ganesan |
MobiSys | 3 |
| 2022 | PhyMask: Robust Sensing of Brain Activity and Physiological Signals During Sleep with an All-textile Eye MaskabstractClinical-grade wearable sleep monitoring is a challenging problem since it requires concurrently monitoring brain activity, eye movement, muscle activity, cardio-respiratory features, and gross body movements. This requires multiple sensors to be worn at different locations as well as uncomfortable adhesives and discrete electronic components to be placed on the head. As a result, existing wearables either compromise comfort or compromise accuracy in tracking sleep variables. We propose PhyMask, an all-textile sleep monitoring solution that is practical and comfortable for continuous use and that acquires all signals of interest to sleep solely using comfortable textile sensors placed on the head. We show that PhyMask can be used to accurately measure all the signals required for precise sleep stage tracking and to extract advanced sleep markers such as spindles and K-complexes robustly in the real-world setting. We validate PhyMask against polysomnography (PSG) and show that it significantly outperforms two commercially-available sleep tracking wearables—Fitbit and Oura Ring. Soha Rostaminia, Seyedeh Zohreh Homayounfar, Ali Kiaghadi, Trisha Andrew, Deepak Ganesan |
ACM Trans. Comput. Heal. | 2 |