Intelligent Mobile Health Monitoring System (IMHMS)

Shahriyar, Rifat; Bari, Md. Faizul; Kundu, Gourab; Ahamed, Sheikh Iqbal; Akbar, Md. Mostofa

doi:10.1007/978-3-642-11745-9_2

Cited by 50 publications

References 5 publications

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“…For example, suppose that a user purchases a medical device compatible with an open platform at a nearby convenience store. Then, the user could use a mobile device to register the medical device purchased in the platform, which enables him to search and select different healthcare services from different medical center [23][24][25][26][27] Suppose that, as shown in Figure 2, members of a family or a social group share medical devices. Let's denote (f,a) to mean member "a" in group "f".…”

Section: Figure 1 Two Different Network Model For U-healthmentioning

confidence: 99%

Registration Protocol for Health IoT Platform to Share the Use of Medical Devices

Lee

2015

IJBSBT

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Section: Figure 1 Two Different Network Model For U-healthmentioning

confidence: 99%

Registration Protocol for Health IoT Platform to Share the Use of Medical Devices

Lee

2015

IJBSBT

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“…Intelligent Mobile Health Monitoring System (IMHMS), [7] Author proposed the system which can provide medical feedback to the patients through mobile devices based on the biomedical and environmental data collected by deployed sensors. The system uses the Wearable Wireless Body/Personal Area Network for collecting data from patients, mining the data, intelligently predicts patient's health status and provides feedback to patients through their mobile devices.…”

Section: IImentioning

confidence: 99%

An Adaptive parameter free data mining approach for healthcare application

Patil¹,

Wadhai²,

Gund³

et al. 2012

IJACSA

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Abstract-In today's world, healthcare is the most important factor affecting human life. Due to heavy work load it is not possible for personal healthcare. The proposed system acts as a preventive measure for determining whether a person is fit or unfit based on his/her historical and real time data by applying clustering algorithms viz. K-means and D-stream. Both clustering algorithms are applied on patient's biomedical historical database. To check the correctness of both the algorithms, we apply them on patient's current biomedical data. The Densitybased clustering algorithm i.e. the D-stream algorithm overcomes drawbacks of K-means algorithm.By calculating their performance measures we finally find out effectiveness and efficiency of both the algorithms.

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“…The algorithm resulted is detection of acute disorders in cows at least 1 d before diagnosis by the farm staff in more than 80% of the cows [4]. Rifat Shahriyar et al (2009) introduced an intelligent mobile health Monitoring System (IMHMS) to monitor the health of cattle, the system could provide patients medical feedback through mobile device [6]. Chakchai So-In et al (2012) developed a prototype with an ATmega16 microcontroller with various sensors , GPS and GSM and used bar signs for mobile monitoring and searching animals [7].…”

Section: Introductionmentioning

confidence: 99%