Abstract
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.
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This research is carried out within the research profile “Embedded Sensor Systems for Health”, funded by the Knowledge Foundation of Sweden.
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© 2016 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
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Xiong, N., Funk, P. (2016). Towards a Probabilistic Method for Longitudinal Monitoring in Health Care. In: Ahmed, M., Begum, S., Raad, W. (eds) Internet of Things Technologies for HealthCare. HealthyIoT 2016. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 187. Springer, Cham. https://doi.org/10.1007/978-3-319-51234-1_5
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DOI: https://doi.org/10.1007/978-3-319-51234-1_5
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