一种实时的跌倒姿态检测和心率监护系统的研究
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摘要
近年来，人体在跌倒状态下相关心率波动变化的研究已逐渐成为生物医学工程界一个热门的课题。其中一个具有重要研究价值的领域是针对常见的老人跌倒导致的健康危害的控制技术，即基于心率和跌倒姿态系统的课题研究。另一方面，无线躯体传感器及其网络技术由于其低功耗、移动性强、安全性高、集成微型化等特点也成为近年来人体医疗监护系统的研究热点。因此，本文提出了基于无线躯体传感器网络的人体跌倒状态及心率检测系统，构建的系统能在短时间内对病人或者老人在跌倒现象下心率变异这一生理参数进行实时性跟踪，实现早期报警。
本文首先构建了便于穿戴的跌倒检测单元、心率检测单元以及无线PDA系统平台，将跌倒现象和突发性心脏疾病有机联系起来分析，该平台也为其它生理监护提供了良好的扩展性。
其次，介绍了人体跌倒识别的研究背景、意义及现状。针对老年人跌倒现象，文中通过实验采集多种不同跌倒模式的特征数据，提出了基于支持向量机(SVM)的跌倒模式识别二分类方法，取得了很好的诊断效果。而针对诊断过程中特征向量维数过多而导致运算复杂度高和存储消耗量大等问题，文中采用成分分析(PCA)做降维处理，降低了识别算法的运算复杂度，进一步提高了系统性能。
文中还在现有的心电信号(ECG)处理技术研究基础上，针对两电极检测出的ECG信号容易受到皮肤移动、肌电伪迹等干扰问题，本文采用小波变换对心电信号进行了消噪预处理，同时也提出了基于最小包围圆的投票表决方法统计心率值，最大程度地捕捉周期性表现的R-R间期，取得了满意的结果。文中还详细分析了MIT-BIH数据库中的心律失常心电数据。实验结果表明，该方法可以准确地提取出心率值。
对于人体健康状态动态监控问题，文中运用无线躯体传感器网络技术实时实地监控人体活动生理状态。文中还针对人体医疗监护特点，基于Energy~*Delay模型的蚁群算法。算法中人工蚂蚁通过在线延迟的方式进行数据交换，并收集邻居节点状态和网络路由信息，以此建立起最佳路由表，使每次传输固定大小的数据和消耗相同能量的情况下使数据包传输时延最小。由于在无线通信系统中，能量消耗和传输时延是两个相互对立特征量，在此运用加强学习的方法RL(Reinforcement Learning)来训练该模型，取得了满意的效果。
本文最后讨论了系统在人体跌倒监护中的应用。通过具体实验方法，对普通性跌倒现象进行了报警试验，取得了90％以上的跌倒报警正确率，并且实时地检测包括跌倒过程在内的所有人体跌倒状态下心率的波动情况，取得了初步的实验效果。
Now people pay more attention to their safety and health while staying in physical activity status, and especially focus on falling which happens on the elderly. And with the development of microelectronics and weak signals detection technique, the research based on integrated systems of heart rete and falling models has already become an important topic as a research direction of biomedical engineering. At the same time, wireless body sensor network technology is being developed as a research hotspot of health monitoring while movements with such the characteristics as energy saving, ad hoc, safety, integration and micromation. The main contents include:
The thesis first introduces the current research status of integration system based on heart rate and falling. After analyzing the virtue and shortcoming of current falling recognition theories, the paper summarizes the heart rate detection while falling and places an emphasis on the most efficient method by detection of R-R periods in two-electrode ECG signals. Chapter 2 introduces the structure and contents of system platform considering the micromation, low power, wearable, high reliability and safety. And then the principle, design methods and corresponding signals extraction technology of each module of the system. Considering the artifical singals such as skin dithering and EMG, the wavelet transform algorithm is employed to extract the QRS waves from the polluted ECG singal, and voting algorithm based on the smallest circle is also brought forward to remove the noises. Several experiments were conducted to give evidence of the robustness and accuracy of the proposed algorithms. For falling phenomenons of the elderly, Chapter 4 discussed the recoginiton technique of plenty of falling models and employed Support Vector Machine (SVM) to binary-classify the models of different activities and recognize falling events. As the shortcoming of complexity and huge memory requirement brought by characteristic vetctors, we used Primary Component Analysis (PCA) to reduce dimensions of vectors space. In the experiment, seven falling patterns were implemented to evaluate the method and obtained satisfying results.
For real-time health monitoring, wireless technology was employed to transfer biomedical data. So all the physiological phenomenons can be synchronized and tracked by wireless body sensor networks. Chapter 5 introduced one network topology suitable for body area, and enhanced the communication protocols including Secuirity and transport layers. Considering a wireless sensor network where the nodes have limited energy, we propose a novel model Energy*Delay based on ant algorithms (E&D ANTS) to minimize time delay in transferring a fixed number of data packets in an energy-constrained manner in one round. However, because of the tradeoff of energy and delay in wireless network systems, the Reinforcement Learning (RL) algorithm is introduced to train the model. The simulation results show that our method performs about seven times better than AntNet and than AntChain by about 150 percent in terms of energy cost and delay per round.
Finally, the thesis discussed some applications about falling monitoring. Many experimental researches of heart rate and falling recongnition are finished, And the ninety percent early-warning ratio are successfully obtained. At the same time, we can track and monitor realtime heart-rate fluctuation curves while subjects move. The primary trial proved successful and effective.
本文首先构建了便于穿戴的跌倒检测单元、心率检测单元以及无线PDA系统平台，将跌倒现象和突发性心脏疾病有机联系起来分析，该平台也为其它生理监护提供了良好的扩展性。
其次，介绍了人体跌倒识别的研究背景、意义及现状。针对老年人跌倒现象，文中通过实验采集多种不同跌倒模式的特征数据，提出了基于支持向量机(SVM)的跌倒模式识别二分类方法，取得了很好的诊断效果。而针对诊断过程中特征向量维数过多而导致运算复杂度高和存储消耗量大等问题，文中采用成分分析(PCA)做降维处理，降低了识别算法的运算复杂度，进一步提高了系统性能。
文中还在现有的心电信号(ECG)处理技术研究基础上，针对两电极检测出的ECG信号容易受到皮肤移动、肌电伪迹等干扰问题，本文采用小波变换对心电信号进行了消噪预处理，同时也提出了基于最小包围圆的投票表决方法统计心率值，最大程度地捕捉周期性表现的R-R间期，取得了满意的结果。文中还详细分析了MIT-BIH数据库中的心律失常心电数据。实验结果表明，该方法可以准确地提取出心率值。
对于人体健康状态动态监控问题，文中运用无线躯体传感器网络技术实时实地监控人体活动生理状态。文中还针对人体医疗监护特点，基于Energy~*Delay模型的蚁群算法。算法中人工蚂蚁通过在线延迟的方式进行数据交换，并收集邻居节点状态和网络路由信息，以此建立起最佳路由表，使每次传输固定大小的数据和消耗相同能量的情况下使数据包传输时延最小。由于在无线通信系统中，能量消耗和传输时延是两个相互对立特征量，在此运用加强学习的方法RL(Reinforcement Learning)来训练该模型，取得了满意的效果。
本文最后讨论了系统在人体跌倒监护中的应用。通过具体实验方法，对普通性跌倒现象进行了报警试验，取得了90％以上的跌倒报警正确率，并且实时地检测包括跌倒过程在内的所有人体跌倒状态下心率的波动情况，取得了初步的实验效果。
Now people pay more attention to their safety and health while staying in physical activity status, and especially focus on falling which happens on the elderly. And with the development of microelectronics and weak signals detection technique, the research based on integrated systems of heart rete and falling models has already become an important topic as a research direction of biomedical engineering. At the same time, wireless body sensor network technology is being developed as a research hotspot of health monitoring while movements with such the characteristics as energy saving, ad hoc, safety, integration and micromation. The main contents include:
The thesis first introduces the current research status of integration system based on heart rate and falling. After analyzing the virtue and shortcoming of current falling recognition theories, the paper summarizes the heart rate detection while falling and places an emphasis on the most efficient method by detection of R-R periods in two-electrode ECG signals. Chapter 2 introduces the structure and contents of system platform considering the micromation, low power, wearable, high reliability and safety. And then the principle, design methods and corresponding signals extraction technology of each module of the system. Considering the artifical singals such as skin dithering and EMG, the wavelet transform algorithm is employed to extract the QRS waves from the polluted ECG singal, and voting algorithm based on the smallest circle is also brought forward to remove the noises. Several experiments were conducted to give evidence of the robustness and accuracy of the proposed algorithms. For falling phenomenons of the elderly, Chapter 4 discussed the recoginiton technique of plenty of falling models and employed Support Vector Machine (SVM) to binary-classify the models of different activities and recognize falling events. As the shortcoming of complexity and huge memory requirement brought by characteristic vetctors, we used Primary Component Analysis (PCA) to reduce dimensions of vectors space. In the experiment, seven falling patterns were implemented to evaluate the method and obtained satisfying results.
For real-time health monitoring, wireless technology was employed to transfer biomedical data. So all the physiological phenomenons can be synchronized and tracked by wireless body sensor networks. Chapter 5 introduced one network topology suitable for body area, and enhanced the communication protocols including Secuirity and transport layers. Considering a wireless sensor network where the nodes have limited energy, we propose a novel model Energy*Delay based on ant algorithms (E&D ANTS) to minimize time delay in transferring a fixed number of data packets in an energy-constrained manner in one round. However, because of the tradeoff of energy and delay in wireless network systems, the Reinforcement Learning (RL) algorithm is introduced to train the model. The simulation results show that our method performs about seven times better than AntNet and than AntChain by about 150 percent in terms of energy cost and delay per round.
Finally, the thesis discussed some applications about falling monitoring. Many experimental researches of heart rate and falling recongnition are finished, And the ninety percent early-warning ratio are successfully obtained. At the same time, we can track and monitor realtime heart-rate fluctuation curves while subjects move. The primary trial proved successful and effective.
引文
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