[1]董振江,谢怡,邓硕,等.个性化远程医疗监护系统研究[J].计算机技术与发展,2013,(05):172-176.
 DONG Zhen-jiang,XIE Yi,DENG Shuo,et al.Research on Personalized Family Remote Monitoring System[J].,2013,(05):172-176.
点击复制

个性化远程医疗监护系统研究()
分享到:

《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
期数:
2013年05期
页码:
172-176
栏目:
应用开发研究
出版日期:
1900-01-01

文章信息/Info

Title:
Research on Personalized Family Remote Monitoring System
文章编号:
1673-629X(2013)05-0172-05
作者:
董振江1谢怡2邓硕1娄梦茜2孙知信2
[1]中兴通讯南京研究所;[2]南京邮电大学 宽带无线通信与传感网教育部重点实验室
Author(s):
DONG Zhen-jiangXIE YiDENG ShuoLOU Meng-qianSUN Zhi-xin
关键词:
远程医疗监护系统支持向量机个人生理模型
Keywords:
remote medical monitoring systemsupport vector machineindividual physical model
文献标志码:
A
摘要:
文中的研究目的是针对当前医疗监护系统不能个性化地反映监护对象身体状况变化等问题,提出一种个性化家庭远程医疗监护系统,对不同年龄、性别、体质的监护对象建立因人而异的监护报警模型.文中的研究方法为通过实时采集人体的体温、心率、血压和血氧饱和度等生理数据,利用支持向量机建立监护对象的生理模型进行诊断.结果显示该模型准确地反映监护对象的身体状况,自动识别出因为传感器误差和监护对象移动所产生的错误报警,同时准确判断出由于生理异常而产生的报警,提高医疗监护系统的诊断效率和准确性
Abstract:
The purpose is for the current health care system can not reflect the change of care object’s physical condition,present a per-sonalized family remote monitoring system,which can build individual alarm model by people’s age,gender and physique. The research method utilizes support vector machine to build object’ s physical model through real-time acquisition of people’ s temperature,heart rate,blood pressure and blood oxygen saturation. The results show that the model can reflect object’s physical condition correctly and au-tomatically identify those false alarms generated by the sensor error and the moving of objects and accurately determine the physiological abnormalities alarm,which improves efficiency and accuracy of diagnosis for the system

相似文献/References:

[1]李雷 张建民.一种改善的基于支持向量机的边缘检测算子[J].计算机技术与发展,2010,(03):125.
 LI Lei,ZHANG Jian-min.An Improved Edge Detector Using the Support Vector Machines[J].,2010,(05):125.
[2]陈俏 曹根牛 陈柳.支持向量机应用于大气污染物浓度预测[J].计算机技术与发展,2010,(01):247.
 CHEN Qiao,CAO Gen-niu,CHEN Liu.Application of Support Vector Machine to Atmospheric Pollution Prediction[J].,2010,(05):247.
[3]李晶 姚明海.基于支持向量机的语义图像分类研究[J].计算机技术与发展,2010,(02):75.
 LI Jing,YAO Ming-hai.Research of Semantic Image Classification Based on Support Vector Machine[J].,2010,(05):75.
[4]姜鹤 陈丽亚.SVM文本分类中一种新的特征提取方法[J].计算机技术与发展,2010,(03):17.
 JIANG He,CHEN Li-ya.A New Feature Selection Method in SVM Text Categorization[J].,2010,(05):17.
[5]曹庆璞 董淑福 罗赟骞.网络时延的混沌特性分析及预测[J].计算机技术与发展,2010,(04):43.
 CAO Qing-pu,DONG Shu-fu,LUO Yun-qian.Chaotic Analysis and Prediction of Internet Time- Delay[J].,2010,(05):43.
[6]路川 胡欣杰.区域航空市场航线客流量预测研究[J].计算机技术与发展,2010,(04):84.
 LU Chuan,HU Xin-jie.Analysis of Regional Airline Passenger Forecast Title[J].,2010,(05):84.
[7]黄炜 黄志华.一种基于遗传算法和SVM的特征选择[J].计算机技术与发展,2010,(06):21.
 HUANG Wei,HUANG Zhi-hua.Feature Selection Based on Genetic Algorithm and SVM[J].,2010,(05):21.
[8]孙秋凤.microRNA计算识别中的模式识别技术[J].计算机技术与发展,2010,(06):97.
 SUN Qiu-feng.Pattern Recognition Technology for MicroRNA Identification[J].,2010,(05):97.
[9]刘振岩 王勇 陈立平 马俊杰 陈天恩.基于SVM的农业智能决策Web服务的研究与实现[J].计算机技术与发展,2010,(06):213.
 LIU Zhen-yan,WANG Yong,CHEN Li-ping,et al.Research and Implementation of Intelligence Decision Web Services Based on SVM for Digital Agriculture[J].,2010,(05):213.
[10]王李冬.一种新的人脸识别算法[J].计算机技术与发展,2009,(05):147.
 WANG Li-dong.A New Algorithm of Face Recognition[J].,2009,(05):147.

更新日期/Last Update: 1900-01-01