Demo Data from SIGIR 2026 / reviewers in the wild / expert
Damoun Nassehi
dblp:185/4016
· DBLP profile ↗
2ranked-venue papers
0as first author
0since 2021 · last 2019
0000-0002-9277-6847ORCID · corroborated
Domains — the database's venue-derived domains; a paper can count in several
Human-computer interaction and ubiquitous computing · 1Applied, interdisciplinary, general and emerging computing · 1
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 · 56% Health and well-being technologies · 44% |
Topics — the 2 heaviest of 3, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Wearable and physiological sensing › vital sign monitoring
breathing sensing |
0.2 | 1 | 2016 | Design and learnability of vortex whistles for managing chronic lung function via smartphones · UbiComp 2016 |
Health and well-being technologies › pulmonary function monitoring
spirometry |
0.2 | 1 | 2016 | Design and learnability of vortex whistles for managing chronic lung function via smartphones · UbiComp 2016 |
Methods — techniques the papers use, named apart from their topics
human subjects trial · 0.23d printing · 0.2
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2019 | Measuring Oxygen Saturation With Smartphone Cameras Using Convolutional Neural NetworksabstractArterial oxygen saturation ([Formula: see text]) is an indicator of how much oxygen is carried by hemoglobin in the blood. Having enough oxygen is vital for the functioning of cells in the human body. Measurement of [Formula: see text] is typically estimated with a pulse oximeter, but recent works have investigated how smartphone cameras can be used to infer [Formula: see text]. In this paper, we propose methods for the measurement of [Formula: see text] with a smartphone using convolutional neural networks and preprocessing steps to better guard against motion artifacts. To evaluate this methodology, we conducted a breath-holding study involving 39 participants. We compare the results using two different mobile phones. We compare our model with the ratio-of-ratios model that is widely used in pulse oximeter applications, showing that our system has significantly lower mean absolute error (2.02%) than a medical pulse oximeter. Xinyi Ding 0001, Damoun Nassehi, Eric C. Larson |
IEEE J. Biomed. Health Informatics | 2 |
| 2016 | Design and learnability of vortex whistles for managing chronic lung function via smartphonesabstractSpirometry is the gold standard for managing and diagnosing obstructive lung diseases. Clinical spirometers, however, are expensive and have limited portability. Vortex whistles have shown promise as a potential substitute for clinical spirometers. While vortex whistles are low-cost and are highly portable, only a subset of common spirometry measurements can be measured reliably. Moreover, no research studies have evaluated characteristics of human interaction with vortex whistles, such as maneuver learnability and mental effort. We present a modified 3D-printed vortex whistle design that enables estimation of spirometry measures not previously attainable with traditional vortex whistles. We evaluate the whistle using a pulmonary waveform generator (a commercial standard) and map parameters of the whistle construction to spirometry test endpoints. Through a human subjects trial we evaluate how to personalize whistle parameters for different subjects and assess cognitive workload while using a vortex whistle. We show that, with personalization, vortex whistles are as effective as clinical spirometers for identifying moderate airway obstruction and require similar cognitive load to use. Spencer A. Kaiser, Ashley Parks, Patrick Leopard, Charlie A. Albright, Jake Carlson, Mayank Goel, Damoun Nassehi, Eric C. Larson |
UbiComp | 7 |