Demonstration venue · read-only. Every page can be browsed; the buttons that would change it are switched off. Create an account to run TaxoReview on your own data. A simulation from public publication records: the people listed were not invited and did not agree to review.
? Tutorial for this page

S. N. Aishwarya

dblp:161/9640 · DBLP profile ↗
← Back
1ranked-venue papers
0as first author
0since 2021 · last 2015
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Computer networks · 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.

Computer networks
1 paper
Internet of things and sensor networks · 100%
Human-computer interaction and pervasive computing
1 paper
Health and well-being technologies · 100%

Topics — the 1 heaviest of 2, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Internet of things and sensor networks › wireless body area network
health monitoring
0.212015
CleanHands: an integrated monitoring system for control of hospital acquired infections · IPSN 2015

Methods — techniques the papers use, named apart from their topics

state-following algorithm · 0.4BLE beacon tracking · 0.4
YearPublicationVenuePosition
2015 CleanHands: an integrated monitoring system for control of hospital acquired infections
abstract
A leading cause of mortality of hospitalized patients are hospital acquired infections (HAI). Unclean hands of healthcare personnel (HCP) are the most common factor contributing to HAI, but their strict compliance to hand hygiene protocols is difficult to supervise. In this work, we propose CleanHands: a simple, low-cost and scalable monitoring and alerting system to ensure adequate thoroughness of disinfection. CleanHands uses a combination of low-cost Bluetooth low energy (BLE) beacon tags and mobile phones for HCP tracking. It integrates infection control models and state-following algorithms for alarming in the event of noncompliance to hand hygiene. Our preliminary experiments in a mockup, small scale intensive care unit (ICU) facility shows promising results with less than 5% false positives.
Prasant Misra, Vasanth Rajaraman, S. N. Aishwarya, Bharat Dwivedi, Jay Warrior
IPSN3