Patient sensors: A data quality perspective

John O'Donoghue, John Herbert, David Sammon

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Wireless sensor devices with communication capabilities are affected by data quality issues. To ensure that information transmitted by these sensing devices are of a high quality, the data needs to be processed, validated and verified to meet the data quality requirements of the end user. The sensor validation component of the Data Management System (DMS) architecture is presented. It is designed to identify if the real-time sensor is functioning within the correct operating bounds. The DMS is applied within a medical environment to assess its ability to manage real-time patient sensor readings. The effectiveness of the DMS-Validation Model (DMS-VM) is evaluated under two real world scenarios 1) Hardware variance among four Tyndall-DMS-Motes with a patient state of resting and 2) One Tyndall-DMS-Mote under three patient states. The experiments have shown the reliability of the Tyndall-DMS-Mote and the ability of the DMS-VM to ensure sensor data quality. Validating sensor reliability is essential to enable safe remote health monitoring in the home.

Original languageEnglish
Title of host publicationSmart Homes and Health Telematics - 6th International Conference, ICOST 2008, Proceedings
Pages54-61
Number of pages8
DOIs
Publication statusPublished - 2008
Externally publishedYes
Event6th International Conference on Smart Homes and Health Telematics, ICOST 2008 - Ames, IA, United States
Duration: 28 Jun 20082 Jul 2008

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume5120 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference6th International Conference on Smart Homes and Health Telematics, ICOST 2008
Country/TerritoryUnited States
CityAmes, IA
Period28/06/082/07/08

Keywords

  • Data quality
  • Geriatric patient monitoring
  • Patient vital sign sensors
  • Pervasive environments

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