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Akash Sethi

dblp:151/0285 · DBLP profile ↗
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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

TopicWeightPapersLastEvidence papers
Wearable and physiological sensing › activity tracking
exercise recognition
0.212014
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
0.212014
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
0.112014
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
YearPublicationVenuePosition
2014 Smart-mat: recognizing and counting gym exercises with low-cost resistive pressure sensing matrix
abstract
There 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
UbiComp4