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基于无线体域网的康复监测系统设计
引用本文:高翔,刘秀鹏,冯天天,徐国政.基于无线体域网的康复监测系统设计[J].微机发展,2014(9):234-237.
作者姓名:高翔  刘秀鹏  冯天天  徐国政
作者单位:南京邮电大学自动化学院,江苏南京210046
基金项目:基金项目:国家自然科学基金资助项目(61104216);江苏省自然科学基金(BK2012832);江苏省高校自然科学基金(12KJB510015);南京邮电大学人才引进基金(NY211020,NY211067)
摘    要:针对目前运动功能康复过程中生理参数监测的不足,基于无线体域网设计并实现了用于康复训练的监测系统。首先设计心电、肌电、脉搏等信号采集模块并在Crossbow平台基于TinyOS系统组建无线体域网;其次基于ARM9和Linux设计本地网关,监控无线体域网,为患者提供图形界面及数据转发;最后基于VC++开发医疗中心监护系统,并结合Matlab采用小波包变换与经验模态分解算法对生理参数进行特征提取及初步诊断。文中详细介绍了系统软件设计,实验结果表明该系统能实现对患者生理状态实时监控等功能。

关 键 词:无线体域网  嵌入式系统  康复监测  特征提取

Monitoring System Design for Rehabilitating Training Based on Wireless Body Area Network
GAO Xiang,LIU Xiu-peng,FENG Tian-tian,XU Guo-zheng.Monitoring System Design for Rehabilitating Training Based on Wireless Body Area Network[J].Microcomputer Development,2014(9):234-237.
Authors:GAO Xiang  LIU Xiu-peng  FENG Tian-tian  XU Guo-zheng
Affiliation:(College of Automation, Nanjing University of Posts and Telecommunications ,Nanjing 210046, China)
Abstract:A monitoring system for rehabilitating training based on wireless body area network is designed to solve the disadvantages existed in traditional physiological parameter monitoring system. Firstly, design signal acquisition model including electrocardiogram, SEMG, pulse parameters and the construct the wireless body area network based on TinyOS on the Crossbow platform. Then, the local gateway is established based on ARM9 and Linux, monitoring the wireless body area network, which provides the patients with graphical interfaces and data transferring. Finally, develop the medical center monitoring system based on VC++, combined with Matlab, the wavelet package transformation and linear discrimination analysis methods are used to extract and recognize the patient' s physiological features. In this paper, the system software design is introduced in detail, experiments results show that the system presented can achieve the real-time monitor for human psychological parameters.
Keywords:wireless body area network  embedded system  rehabilitation monitoring  feature extraction
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