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Towards a probabilistic method for longitudinal monitoring in health care
Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.ORCID iD: 0000-0001-9857-4317
Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.ORCID iD: 0000-0002-5562-1424
2016 (English)In: The 3rd EAI International Conference on IoT Technologies for HealthCare HealthyIoT'16, 2016, Vol. 187, 30-35 p.Conference paper (Refereed)
Abstract [en]

The advances in IoT and wearable sensors enable long term monitoring, which promotes earlier and more reliable diagnosis in health care. This position paper proposes a probabilistic method to address the challenges in handling longitudinal sensor signals that are subject to stochastic uncertainty in health monitoring. We first explain how a longitudinal signal can be transformed into a Markov model represented as a matrix of conditional probabilities. Further, discussions are made on how the derived models of signals can be utilized for anomaly detection and classification for medical diagnosis.

Place, publisher, year, edition, pages
2016. Vol. 187, 30-35 p.
Series
ecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST, ISSN 1867-8211
Keyword [en]
health monitoring, longitudinal signal, symbolic time series, Markovmodel, case-based reasoning
National Category
Computer Systems
Identifiers
URN: urn:nbn:se:mdh:diva-33821DOI: 10.1007/978-3-319-51234-1_5ScopusID: 2-s2.0-85011277200OAI: oai:DiVA.org:mdh-33821DiVA: diva2:1048583
Conference
The 3rd EAI International Conference on IoT Technologies for HealthCare HealthyIoT'16, 18 Oct 2016, Västerås, Sweden
Projects
ESS-H - Embedded Sensor Systems for Health Research Profile
Available from: 2016-11-21 Created: 2016-11-21 Last updated: 2017-02-16Bibliographically approved

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CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf