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Alternative TitleResearch on Healthcare System Technologies based on Body Sensor Network
Thesis Advisor杨一平 ; 李学恩
Degree Grantor中国科学院研究生院
Place of Conferral中国科学院自动化研究所
Degree Discipline计算机应用技术
Keyword身体传感网 心电图 三轴加速度 支持向量机 健康监护 Body Sensor Network Ecg Tri-axial Accelerometer Svm Healthcare System
Abstract健康监护是无线传感器网络的重要应用领域之一。基于身体传感网的健康监护系统融合了无线传感器网络有线网络及远距离无线通讯等多种技术。其中研究小体积、低功耗的传感器节点和降低无线通讯的功耗是难点所在。基于身体传感网的健康监护系统相对于其他健康监护设备的优势在于在对被监护者的健康状况进行监护的同时不影响日常活动,这一特点在方便被监护者的同时也对健康监护数据的采集和自动诊断算法的抗干扰性能带来了更大的挑战,同时也提出了如何识别被监护者的活动背景的问题。 基于上述,本文在构建基于身体传感网的健康监护系统的基础上,对心电图的自动识别算法、活动背景识别算法展开了研究。主要工作有以下几部分: 第一,分析了基于身体传感网的健康监护系统体系架构,对体系架构从功耗和带宽的角度进行了优化。 第二,设计了一种基于片上系统处理器MC13213和PXA270 Xscale处理器的符合IEEE802.15.4协议的身体传感网健康监护软硬件系统。节点具有低功耗、小体积和可扩展性等特点。 第三,对IEEE801.15.4协议MAC层进行了深入研究,利用仿真结果提出了适合健康监护应用特点的MAC协议参数;并根据身体传感网的特点提出了自适应节点优先级控制策略和节点的发送功率控制策略,降低了节点通讯的功耗。 第四,对基于多种小波变换的心电图降噪预处理和特征识别算法进行了研究。小波变换的降噪是基于提升格式的,算法的计算速度快,占用内存小,在取得较好的滤波效果的同时也有利于进一步向嵌入式系统移植。在特征识别方面,QRS波的识别目前研究的较多,而P、T波的识别目前还比较困难,本文提出了一种基于小波变换的P波、T波识别算法。 第五,设计了基于多类支持向量机算法的利用单三轴加速度传感器进行日常活动识别方法。
Other AbstractHealthcare is one of the important applying fields of wireless sensor network. The technologies of body Sensor Network based healthcare system include wireless sensor network, wired network and long-distance wireless communication. And design of small size, low consumption, low price sensor node is prerequisite for the technique. The BSN healthcare system is characterized by monitoring paitent influence the patients' activities of daily living relative to other healthcare devices. This feature brings convenience to users, and also more challenge for sample of vital signals and automatic diagnosis algorithm. It also brings the problem of activity recognition. According to above description, a healthcare platform is designed based on body sensor network. ECG characteristic points recognition algorithm, activity recognition algorithm is designed. The work mainly consists of the following aspects: Firstly, an improved architecture of body sensor network is put forward. The body sensor network based on this architecture would decrease the bandwidth of single user. Secondly, a body sensor network system is designed based on MC13213 SOC processors, PXA270 Xscale processor and IEEE 802.15.4 protocol stack. The sensor node has characteristics as follows: low power consumption, small size and extensibility. Thirdly, IEEE 802.15.4 MAC protocol is deeply analyzed. The MAC parameter suitable to healthcare system is proposed based on simulation. Application layer protocol and program is designed. Fourthly ECG denoising algorithm and characteristic points algorithm is research based on multi-class wavelet. The algorithm of wavelet denoising uses a lifting scheme wavelet transform. So the algorithm has characteristics with fast calculating speed, less memory. So the denoising algorithm is favorable to running in embedded system. Fifthly, Activity of daily living recognition used one triaxial accelerator sensor is designed based on multi-class support vector machines algorithm.
Other Identifier200418014628023
Document Type学位论文
Recommended Citation
GB/T 7714
韩现会. 基于身体传感网的健康监护系统研究[D]. 中国科学院自动化研究所. 中国科学院研究生院,2008.
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