Human health control monitor system using smart mobiles: Context changes dependent human behavior

Ashok Senthil Kumar, Subash Chandar, PG Students
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引用次数: 1

Abstract

A Human health control monitor system (HHCMSes) is a mobile medical application is used to detect continuous monitoring and manage the human body blood glucoses levels. HHCMS promises to give to extend the life period of human being. HHCMSes is a testing device very efficient to monitor the glucoses levels. Sensor can wear anywhere in our human body as like as wrist watches. A sensor connects to smart phones using HHCMSes software. Once connected this software continuously to monitor the blood glucoses levels in our body. In our body, when change the glucose levels is to intimate through smart mobile phones. HHCMS is easily to monitor the indoor and outdoor physical movement of blood levels changing often in our body. Existing approaches uses moment generate function (MGF) is support only statistical method of gathering the value, reviewing the value, survey the value, and give an interpreting the variable of numerical data, cannot identifying the transfer function between sensor and mobiles. In this paper, we can propose robust compatible, so we can uses Mason's gain formula (MGF) is a process to detect the transfer function by using linear signal-flow graph (SFG) and cyebyshev's theorem and Markov theorem uses to find out the desired result of HHCMSes.
使用智能手机的人类健康控制监测系统:环境变化依赖于人类行为
人体健康控制监测系统(HHCMSes)是一种移动医疗应用程序,用于检测、监测和管理人体血糖水平。健康医疗保险承诺延长人类的生命周期。HHCMSes是一种非常有效的血糖监测设备。传感器可以像手表一样佩戴在人体的任何部位。传感器通过HHCMSes软件连接到智能手机。一旦连接上这个软件,就可以持续监测我们体内的血糖水平。在我们的身体里,当改变血糖水平是通过智能手机来实现的。HHCMS很容易监测室内和室外的身体运动,血液水平经常在我们体内变化。现有的方法使用矩生成函数(MGF),只支持收集值、审查值、调查值和给出数值数据变量解释的统计方法,不能识别传感器与移动设备之间的传递函数。在本文中,我们可以提出鲁棒兼容,因此我们可以使用Mason增益公式(MGF)是一个检测传递函数的过程,利用线性信号流图(SFG)和cyebyshev定理和Markov定理来找出HHCMSes的期望结果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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