Towards a probabilistic method for longitudinal monitoring in health care
2016 (English)In: The 3rd EAI International Conference on IoT Technologies for HealthCare HealthyIoT'16, 2016, Vol. 187, 30-35 p.Conference paper (Refereed)
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.
ecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST, ISSN 1867-8211
health monitoring, longitudinal signal, symbolic time series, Markovmodel, case-based reasoning
IdentifiersURN: 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
The 3rd EAI International Conference on IoT Technologies for HealthCare HealthyIoT'16, 18 Oct 2016, Västerås, Sweden
ProjectsESS-H - Embedded Sensor Systems for Health Research Profile