Internet of Things Technologies for HealthCare. Third International Conference, HealthyIoT 2016, Västerås, Sweden, October 18-19, 2016, Revised Selected Papers

Research Article

Towards a Probabilistic Method for Longitudinal Monitoring in Health Care

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  • @INPROCEEDINGS{10.1007/978-3-319-51234-1_5,
        author={Ning Xiong and Peter Funk},
        title={Towards a Probabilistic Method for Longitudinal Monitoring in Health Care},
        proceedings={Internet of Things Technologies for HealthCare. Third International Conference, HealthyIoT 2016, V\aa{}ster\ae{}s, Sweden, October 18-19, 2016, Revised Selected Papers},
        proceedings_a={HEALTHYIOT},
        year={2017},
        month={1},
        keywords={Health monitoring Longitudinal signal Symbolic time series Markov model Case-based reasoning},
        doi={10.1007/978-3-319-51234-1_5}
    }
    
  • Ning Xiong
    Peter Funk
    Year: 2017
    Towards a Probabilistic Method for Longitudinal Monitoring in Health Care
    HEALTHYIOT
    Springer
    DOI: 10.1007/978-3-319-51234-1_5
Ning Xiong1,*, Peter Funk1,*
  • 1: Mälardalen University
*Contact email: ning.xiong@mdh.se, peter.funk@mdh.se

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.