Demo Data from SIGIR 2026 / reviewers in the wild / expert
Akash Sethi
dblp:151/0285
· DBLP profile ↗
1ranked-venue papers
0as first author
0since 2021 · last 2014
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Human-computer interaction and ubiquitous 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 · 87% Health and well-being technologies · 13% |
Topics — the 3 heaviest of 3, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Wearable and physiological sensing › activity tracking
exercise recognition |
0.2 | 1 | 2014 | Smart-mat: recognizing and counting gym exercises with low-cost resistive pressure sensing matrix · UbiComp 2014 |
Wearable and physiological sensing › pressure sensing
pressure sensor matrix |
0.2 | 1 | 2014 | Smart-mat: recognizing and counting gym exercises with low-cost resistive pressure sensing matrix · UbiComp 2014 |
Health and well-being technologies › physical activity
exercise monitoring |
0.1 | 1 | 2014 | Smart-mat: recognizing and counting gym exercises with low-cost resistive pressure sensing matrix · UbiComp 2014 |
Methods — techniques the papers use, named apart from their topics
textile pressure sensing · 0.2classification · 0.2
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2014 | Smart-mat: recognizing and counting gym exercises with low-cost resistive pressure sensing matrixabstractThere is a large class of routine physical exercises that are performed on the ground, often on dedicated "mats" (e.g. push-ups, crunches, bridge). Such exercises involve coordinated motions of different body parts and are difficult to recognize with a single body worn motion sensors (like a step counter). Instead a network of sensors on different body parts would be needed, which is not always practicable. As an alternative we describe a cheap, simple textile pressure sensor matrix, that can be unobtrusively integrated into exercise mats to recognize and count such exercises. We evaluate the system on a set of 10 standard exercises. In an experiment with 7 subjects, each repeating each exercise 20 times, we achieve a user independent recognition rate of 82.5% and a user independent counting accuracy of 89.9%. The paper describes the sensor system, the recognition methods and the experimental results. Mathias Sundholm, Jingyuan Cheng, Bo Zhou 0005, Akash Sethi, Paul Lukowicz |
UbiComp | 4 |