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E-health monitoring system enhancement with Gaussian mixture model

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Abstract

In order to enhance the healthcare system, we have designed and developed a system prototype which remotely monitors patient’s vital parameters by using mobile based android application. Proposed E-health care system collects patient’s biological and personal information with the corresponding vital parameters and stores this Meta data information into the health care database servers. The distributed servers are connected with GSP system. So the extracted information from the server is directly feed to the doctor’s mobile device as well as to the patient's mobile devices in a presentable format. This system also uses Frontline SMS as an SMS service which is used to send SMS to the doctor’s mobile device automatically, when any one of the patient’s vital parameter goes out of normal range. In this paper, we present the GMM (Gaussian mixture model) based on extracted features of the patient information and assign it to the specialized doctor. In this work, we have shown that by GMM based algorithm efficiently balances the patient load to the doctor. This novel approach enhances the E-health monitoring system for normal situations as well as in the case of Natural disaster. The proposed load balancing approach gives relief to the patient for unnecessary long delay to receive medical advice. The presented result in this work shown that, the doctors from all category and specialization are loaded rationally and uniformly. According to our knowledge GMM based approach is the new additional component to enhance the E-health care system.

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Acknowledgments

The authors are grateful to Sharmista Das Kanrar from Bishop Westcott, Ranchi, India.

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Authors and Affiliations

  1. Vehere Interactive Pvt Ltd, Calcutta, WB, India

    Soumen Kanrar & Prasenjit Kumar Mandal

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  1. Soumen Kanrar
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  2. Prasenjit Kumar Mandal
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Correspondence to Soumen Kanrar.

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Kanrar, S., Mandal, P.K. E-health monitoring system enhancement with Gaussian mixture model. Multimed Tools Appl 76, 10801–10823 (2017). https://doi.org/10.1007/s11042-016-3509-9

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  • DOI: https://doi.org/10.1007/s11042-016-3509-9

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