phat 15(4): e2

Research Article

COMPASS: an Interoperable Personal Health System to Monitor and Compress Signals in Chronic Obstructive Pulmonary Disease

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  • @ARTICLE{10.4108/icst.pervasivehealth.2015.259186,
        author={Thomas Hofer and Michael Schumacher and Stefano Bromuri},
        title={COMPASS: an Interoperable Personal Health System to Monitor and Compress Signals in Chronic Obstructive Pulmonary Disease},
        journal={EAI Endorsed Transactions on Pervasive Health and Technology},
        volume={1},
        number={4},
        publisher={EAI},
        journal_a={PHAT},
        year={2015},
        month={8},
        keywords={copd, compression, interoperability, soa, mobile},
        doi={10.4108/icst.pervasivehealth.2015.259186}
    }
    
  • Thomas Hofer
    Michael Schumacher
    Stefano Bromuri
    Year: 2015
    COMPASS: an Interoperable Personal Health System to Monitor and Compress Signals in Chronic Obstructive Pulmonary Disease
    PHAT
    EAI
    DOI: 10.4108/icst.pervasivehealth.2015.259186
Thomas Hofer1, Michael Schumacher1, Stefano Bromuri1,*
  • 1: University of Applied Sciences Western Switzerland
*Contact email: stefanobromuri@googlemail.com

Abstract

In the past years the progress on the mobile market has made possible an advancement in terms of telemedicine systems and definition of systems for monitoring chronic illnesses. The distribution of mobile devices in developed countries is increasing. Many of these devices are equipped with wireless standards including Bluetooth and the amount of sold Smartphones is constantly increasing. Our approach is oriented towards this market, using existing devices to enable in-home patient monitoring and even further to ubiquitious monitoring. The idea is to increase the quality of care, reduce costs and gather medical grade data, especially vital signs, with a resolution of minutes or even less, which is nowadays only possible in an ICU (Intensive Care Units). In this paper we will present the COMPASS personal health system (PHS) platform, and how this platform enables Android devices to collect, analyze and send sensor data to an observation storage by means of interoperability standards. Furthermore, we will also present how this data can be compressed using advanced compressed sensing techniques and how to optimize these techniques with genetic algorithms to improve the RMSE of the reconstructed signal after compression. We also produce a preliminary evaluation of the algorithm against the state of the art algorithms for compressed sensing.