Biometric monitoring system based on K-means ＆MTLS-SVM algorithm

Jingming XIA; Lingling TANG; Ling TAN; Han ZHENG

doi:10.11959/j.issn.1000-0801.2017286

您当前的位置：

首页 >

文章列表页 >

Biometric monitoring system based on K-means ＆MTLS-SVM algorithm

Topic:Internet of things technology and application | 更新时间：2024-06-05

- Biometric monitoring system based on K-means ＆MTLS-SVM algorithm
- Telecommunications Science Vol. 33, Issue 10, Pages: 43-49(2017)
- 作者机构：
- 作者简介：
- 基金信息：
- DOI：10.11959/j.issn.1000-0801.2017286
  CLC： TN919
- Published Online：2017-10，
  
  Published：20 October 2017
- 稿件说明：
移动端阅览
Jingming XIA, Lingling TANG, Ling TAN, et al. Biometric monitoring system based on K-means ＆MTLS-SVM algorithm[J]. Telecommunications science, 2017, 33(10): 43-49.
DOI：

Jingming XIA, Lingling TANG, Ling TAN, et al. Biometric monitoring system based on K-means ＆MTLS-SVM algorithm[J]. Telecommunications science, 2017, 33(10): 43-49. DOI： 10.11959/j.issn.1000-0801.2017286.

摘要

在非医模式的生理参数监测系统中，对监测参数进行学习，可以提高诊断和预测精度。针对多任务时间序列中存在的信息挖掘不充分、预测精度低等问题，将机器学习中的监督和半监督学习方式结合起来对远程健康监护对象进行生理状况预测。该方法用 K-means 算法将相同类别的数据集群，并使用多任务最小二乘支持向量机（MTLS-SVM）来训练历史数据来进行趋势预测。为了评估该方法的有效性，将MTLS-SVM方法与K-means、MTLS-SVM方法比较，实验结果表明该方法具有较高的预测精度。

Abstract

In a nonmedical biometric monitoring system

the monitoring parameters are preceded with machine learning for precision promotion of diagnosis and prediction.Considering the problems of insufficient information mining and low prediction accuracy in multi task time series

both supervised and unsupervised machine learning techniques were applied to predict the physical condition of the remote health care.These techniques were K-means for clustering the similar group of data and MTLS-SVM model for training and testing historical data to perform a trend prediction.In order to evaluate the effectiveness of the method

the proposed method was compared with MTLS-SVM method.The experimental results show that the proposed method has higher prediction accuracy.

关键词

Keywords

references

褚航 , 曾碧 . 非接触式心跳监控系统——面向社区医疗服务的物联网应用系统研究与开发 [J ] . 计算机系统应用 , 2012 , 21 ( 8 ): 233 - 235 .

CHU H , ZENG B . Non contact heartbeat monitoring system—research and development of internet of things application system for community medical service [J ] . Computer System ＆Applications , 2012 , 21 ( 8 ): 233 - 235 .

谈玲 , 张琭 . 交互式多生物特征识别技术在电子商务中的应用 [J ] . 电信科学 , 2015 , 31 ( 10 ): 124 - 129 .

TAN L , ZHANG L . Application of interactive multi-biometrics recognition on E-commerce [J ] . Telecommunications Science , 2015 , 31 ( 10 ): 124 - 129 .

PALANIAPPAN S , AWANG R . Intelligent heart disease prediction system using data mining techniques [C ] // International Conference on Computer Systems ＆ Applications,March 30-April 3,2008,Doha,Qatar . New Jersey:IEEE Press , 2008 : 108 - 115 .

TSIPOURAS M G , EXARCHOS T P , FOTIADIS D I , et al . Automated diagnosis of coronary artery disease based on data mining and fuzzy modeling [J ] . IEEE Transactions on Information Technology in Biomedicine , 2008 , 12 ( 4 ): 447 - 458 .

PATIL S B , KUMARASWAMY Y S . Intelligent and effective heart attack prediction system using data mining and artificial neural network [J ] . European Journal of Scientific Research , 2009 , 31 ( 4 ): 642 - 656 .

SRINIVAS K , KAVITHA R B K , GOVRDHAN A . Applications of data mining techniques in healthcare and prediction of heart attacks [J ] . International Journal on Computer Science and Engineering , 2010 , 2 ( 2 ): 250 - 255 .

RESUL D , TURKOGLU I , SENGUR A . Effective diagnosis of heart disease through neural networks ensembles [J ] . International Journal of Expert Systems with Applications , 2009 ( 36 ): 7675 - 7680 .

VAPNIK V . The nature of statistical learning theory [M ] . New York : SpringerPress , 1995 .

SAPANKEVYCH N I , SANKAR R . Time series prediction using support vector machines:a survey [J ] . IEEE Computational Intelligence Magazine , 2009 , 4 ( 2 ): 24 - 38 .

KAYTEZ F , TAPLAMACIOGLU M C , CAM E , et al . Forecasting electricity consumption:a comparison of regression analysis,neural networks and least squares support vector machines [J ] . International Journal of Electrical Power ＆ Energy Systems , 2015 ( 67 ): 431 - 438 .

VAN G T , SUYKENS J K , BAESTAENS D E , et al . Financial time series prediction using least squares support vector machines within the evidence framework [J ] . IEEE Transactions on Neural Networks , 2001 , 12 ( 4 ): 809 - 821 .

YE M Y , WANG X D . Chaotic time series prediction using least squares support vector machines [J ] . Chinese Physics , 2004 , 13 ( 4 ):454.

CARUANA R . Multitask learning [M ] . New York : SpringerPress , 1998 : 95 - 133 .

HAN J W , KAMBER M . Data mining:concepts and techniques:2nd edition [J ] . San Francisco:Morgan Kaufman Publishers , 2006 .

Views

648

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

Research on person re-identification algorithm based on multi-task learning

Customer service complaint work order classification based on matrix factorization and attention multi-task learning

Related Author

MI Rongxin

YAO Wenwen

WU Binghao

Yong SONG

Zhiwei YAN

Yukun QIN

Dongming ZHAO

Xiaozhou YE

Related Institution

National Computer Network Emergency Response Technical Team/Coordination Center of China

School of Computer Science, Beijing University of Posts and Telecommunications

AsiaInfo Technologies (China) Co., Ltd.

AsiaInfo Technologies (Nanjing) Co., Ltd.

China Mobile Communications Group Tianjin Co., Ltd.

⁰