移动健康监护跟踪系统的设计

发布时间：2019-05-24 17:10

【摘要】：我国是世界上人口最多的国家,近几年我国的老年人口逐渐增多,并向老龄化社会迈进。而老年人由于自身生理因素的影响,身体机能不断衰减,在日常活动中往往会遇到一些麻烦,甚至是突发状况如摔倒、中风等,如果发生这些异常的情况而得不到及时救助的话,将会发生不可挽回的悲剧。因此解决老年人的行动安全和医疗保健问题逐渐成为热门的研究领域。基于以上背景研究并设计了一种移动健康监护跟踪系统。本文主要研究内容如下:首先,从动力学的角度对人体日常活动和摔倒过程进行了受力分析,根据人体日常活动和摔倒时的受力差别,分析了SVM阈值检测算法。应用小波变换对心电数据进行去噪,再利用心电信号数据库MIT-BIH中的心电信号数据进行去噪仿真实验。其次,根据现有的摔倒检测算法和心电信号特征值提取算法,提出了一种基于小波变换的心电信号摔倒检测算法。单片机利用SVM阈值检测算法判断人体疑似摔倒,若人体为疑似摔倒状态,就给手机发送FF信号;手机根据FF信号开始接收心电信号数据,利用小波变换对心电信号数据进行特征提取;利用正常心电信号特征值和疑似摔倒时心电信号的特征值进行对比分析,进而对人体摔倒状态进行判定。再次,完成了移动健康监护跟踪系统的设计,包括硬件和软件两部分。硬件部分主要包括心电信号采集模块、三维变量采集模块和蓝牙模块,其中心电信号采集模块采集人体的心率数据,三维变量采集模块采集人体三个轴向的加速度,蓝牙模块实现硬件与手机的数据通信。软件部分主要包括心电信号数据接收模块、心电波形显示模块、心电信号特征值提取模块、心电信号摔倒检测模块、GPS地理位置获取模块以及短信示警模块。最后,对系统各个模块进行了实验测试,并对系统进行人体摔倒测试和人体不同运动状态误报测试。实验结果表明,本系统能够有效的判断出人体是否为摔倒状态,且具有实时性和便携性,但不适合对正在进行剧烈活动的人体进行摔倒检测。
[Abstract]:China is the most populous country in the world. In recent years, the elderly population in China has gradually increased and is moving forward to an aging society. However, due to the influence of their own physiological factors, the physical function of the elderly continues to decline, and they often encounter some problems in their daily activities, even sudden situations such as falls, strokes, and so on. If these abnormal situations are not rescued in a timely manner, irreparable tragedies will occur. Therefore, solving the problem of mobile safety and health care for the elderly has gradually become a hot research field. Based on the above background, a mobile health monitoring and tracking system is designed. The main contents of this paper are as follows: firstly, the stress analysis of human daily activity and fall process is carried out from the point of view of dynamics, and the SVM threshold detection algorithm is analyzed according to the difference of human daily activity and falling force. Wavelet transform is used to Denoise ECG data, and then ECG data in ECG database MIT-BIH are used to Denoise simulation experiment. Secondly, according to the existing fall detection algorithm and ECG signal eigenvalue extraction algorithm, an ECG fall detection algorithm based on wavelet transform is proposed. Single chip microcomputer uses SVM threshold detection algorithm to judge the suspected fall of the human body, if the human body is a suspected fall state, it will send the FF signal to the mobile phone. According to the FF signal, the mobile phone begins to receive ECG signal data, and wavelet transform is used to extract the ECG signal data. The eigenvalues of normal ECG signals and suspected falls are compared and analyzed, and then the falling state of human body is judged. Thirdly, the design of mobile health monitoring and tracking system is completed, including hardware and software. The hardware part mainly includes ECG signal acquisition module, three-dimensional variable acquisition module and Bluetooth module. Its central electrical signal acquisition module collects human heart rate data, and three-dimensional variable acquisition module collects three axial acceleration of human body. Bluetooth module realizes the data communication between hardware and mobile phone. The software mainly includes ECG data receiving module, ECG waveform display module, ECG eigenvalue extraction module, ECG fall detection module, GPS geographical location acquisition module and SMS alarm module. Finally, the experimental tests are carried out on each module of the system, and the human fall test and the false alarm test of different motion states of the system are carried out. The experimental results show that the system can effectively judge whether the human body is a fall state, and has real-time and portability, but it is not suitable for the fall detection of the human body who is engaged in strenuous activity.
【学位授予单位】：兰州交通大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：TP274

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