贝叶斯算法在人体生理状态识别中的应用
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摘要
目前,随着社会科技的不断进步,我国的医疗服务行业发展迅速,极大的满足了广大人民群众日益增长的医疗消费需求。在当前信息科技不断进步,无线信息传送网络不断完善的同时,医疗卫生领域出现了“远程医疗”这一崭新概念,随之引起了医疗行业的重大变革。远程医疗就是在这种医疗和信息技术不断进步的背景下出现的,是医疗水平和信息技术不断完善的产物。远程医疗的出现为人类提供了更加迅速便捷的医疗服务。据相关资料报道,目前老年人、慢性疾病患者及术后病人的日常监护问题已经成为当前远程医疗服务行业的一个热点问题。
本文就远程医疗监护中的人体行为跟踪这一问题进行了一些研究,主要对人体的日常生理状态做实时的监控并对一些异常的步态信息及时报警。同时将人体的一些重要日常行为信息和生理信息做出记录,保存到个人专有数据库中。这样就为将来的医生就诊提供了良好的诊断依据。在上述行为识别监护过程中我们将贝叶斯智能算法引入到行为和步态的识别中,取得了不错的识别效果。
本文的人体行为识别过程首先通过无线人体局域网采集人体生理信号,然后对采集来的人体生理信号进行分类处理和判断,将信号离散化。再结合贝叶斯算法达到对一些常见人体生理状态的识别。实验证明,该贝叶斯算法能够有效的进行人体生理状态的识别监控。并且该算法简单可靠,对原始信号数据的要求不高却能达到较高的识别率。此外该算法也可以应用到BSN领域的其他方面。
At present, with constant progress of scientist and technology, China's health care industry has been developed rapidly, which meets the growing demand of people for medical services. Meanwhile, the improvements of information technology and wireless information network caused a major change in the area of medical services. The concept of tele-medicine appears in this background. Tele-medicine can provide even more rapid and convenient medical services through the wireless network technology and the current level of advanced medical care for human beings. It is reported, currently, the custody of the elderly, chronically ill patients and patients after operation has become a major problem in the social health care industry.
Some research on the tracking of human behavior of Tele-medicine has been done in this thesis, which includes real-time monitoring and recording of the conduct of human body's day-to-day state and warning when abnormal gait information is to be found. At the same time, to do a record of day-to-day information and physiological information and the preservation of individual proprietary database will provide a good basis for the diagnosis in the future of the medical treatment. It has made good identify results, which advanced intelligent Bayesian algorithm into the behavior and gait recognition in the above mentioned tele-monitoring process.
In this thesis, the process of identification of human behavior includes collecting physiological signals through the wireless local area network firstly, and then classifying the collection of physiological signals, conversing victory signals into discrete ones, at last achieving some common recognition of the state of human behavior combined with a Bayesian algorithm. The experiments have proved that the method using Bayesian algorithm has been greatly improved recognition accuracy of human act. The algorithm is simple and reliable, asking with less original data signal but higher recognition rate. In addition, the method can be applied to other areas of BSN.
本文就远程医疗监护中的人体行为跟踪这一问题进行了一些研究,主要对人体的日常生理状态做实时的监控并对一些异常的步态信息及时报警。同时将人体的一些重要日常行为信息和生理信息做出记录,保存到个人专有数据库中。这样就为将来的医生就诊提供了良好的诊断依据。在上述行为识别监护过程中我们将贝叶斯智能算法引入到行为和步态的识别中,取得了不错的识别效果。
本文的人体行为识别过程首先通过无线人体局域网采集人体生理信号,然后对采集来的人体生理信号进行分类处理和判断,将信号离散化。再结合贝叶斯算法达到对一些常见人体生理状态的识别。实验证明,该贝叶斯算法能够有效的进行人体生理状态的识别监控。并且该算法简单可靠,对原始信号数据的要求不高却能达到较高的识别率。此外该算法也可以应用到BSN领域的其他方面。
At present, with constant progress of scientist and technology, China's health care industry has been developed rapidly, which meets the growing demand of people for medical services. Meanwhile, the improvements of information technology and wireless information network caused a major change in the area of medical services. The concept of tele-medicine appears in this background. Tele-medicine can provide even more rapid and convenient medical services through the wireless network technology and the current level of advanced medical care for human beings. It is reported, currently, the custody of the elderly, chronically ill patients and patients after operation has become a major problem in the social health care industry.
Some research on the tracking of human behavior of Tele-medicine has been done in this thesis, which includes real-time monitoring and recording of the conduct of human body's day-to-day state and warning when abnormal gait information is to be found. At the same time, to do a record of day-to-day information and physiological information and the preservation of individual proprietary database will provide a good basis for the diagnosis in the future of the medical treatment. It has made good identify results, which advanced intelligent Bayesian algorithm into the behavior and gait recognition in the above mentioned tele-monitoring process.
In this thesis, the process of identification of human behavior includes collecting physiological signals through the wireless local area network firstly, and then classifying the collection of physiological signals, conversing victory signals into discrete ones, at last achieving some common recognition of the state of human behavior combined with a Bayesian algorithm. The experiments have proved that the method using Bayesian algorithm has been greatly improved recognition accuracy of human act. The algorithm is simple and reliable, asking with less original data signal but higher recognition rate. In addition, the method can be applied to other areas of BSN.
引文
[1]李义兵.我国医疗信息系统现状及面临的主要问题[J].现代医院,2005,5(9):135-136.
[2]俞梦孙.关于我国医学信息技术的发展[J].中国生物医学工程学报,2008,27(2):161-163.
[3]Benny lo.Body Sensor Networks-research challenges and opportunities[C].Proceedings of the international Workshop on Wearable and implantable Body Sensor Networks,London,2006:7-15.
[4]Omer Aziz,Benny lo.An Approach to Monitoring the Post-operative Surgical Patient[C].Proceedings of the international Workshop on Wearable and implant table Body Sensor Networks,London,2006:41-47.
[5]许剑,卢建刚.多参数无线医疗监护系统的设计与开发[J].中国医疗器械杂志,2005,29(6):406-409.
[6]胡广书.现代信号处理教程[M].北京:清华大学出版社,2004.
[7]Borkar S.Design challenges of technology scaling[C].IEEE Micro,1999,19(4):23-29.
[8]Culler S,Estrin D.Overview of sensor networks[C].Computer 2004,37(8):41-49.
[9]Warneke B,last M,Pister KSJ.Smart dust:communicating with a cubic-millimeter[A].Computer 2001,34(1):44-51.
[10]林媛,陈新.面向对象的医疗诊断推理机设计[J].计算机应用与软件,2001,18(1):5-9.
[11]王希军,程严.远程医疗诊断现状[M].北京:人民邮电出版社,2005.
[12]Izawa KP,YamadaS,OkaKetal.Long-term exercise maintenance physical activity and health-related quality of life after cardiac rehabilitation[C].International Workshop on Wearable and Implantable Body Sensor Networks,London,2005:131-135.
[13]Lo BPL,Yang GZ.Key Technical Challenges and Current Implementations of Body Sensor Networks[C].International Workshop on Wearable and Implantable Body Sensor Networks,London,2005:71-77.
[14]Julien Pansiot,Benny Lo,Guang-Zhong Yang.Towards Image-Based Modeling for Ambient Sensing[C].Proceedings of the International Workshop on Wearable and Implantable Body Sensor Networks,London,2006:83-89.
[15]Surapa Thiemjars,Benny Lo,Guang-Zhong Yang.A Spatio-Temporal Architecture for Context Aware Sensing[C].Proceedings of the International Workshop on Wearable and Implantable Body Sensor Networks,London,2006:171-176.
[16]Surapa Thiemjarus,Rachel King,Benny Lo.A noise resilient distributed inference framework for Body sensor Networks[C].International Workshop on Wearable and Implantable Body Sensor Networks,London,2006:101-107.
[17]BennyLo,Louis,AraDarzi.Real-Time Pervasive Monitoring for Postoperative Care[C].Proceedings of the International Workshop on Wearable and Implantable Body Sensor Networks,London,2006:156-161.
[18]Benny Lo,Guang-Zhong Yang.The perspective of Body sensor Networks[C].Proceedings of the International Workshop on Wearable and Implantable Body Sensor Networks,London,2006:91-96.
[19]An Introduction to Bayesian Networks and their Contemporary Applications,reference:http://www.niedermayer.ca/index.html.
[20]徐从富.浙江大学计算机科学学院.《人工智能》 课件:第七章,贝叶斯网络,54-97.
[21]冀俊忠,刘椿年,阎静.一种快速的贝叶斯网结构学习算法[M].北京:北京工业大学出版社,2007.
[22]徐建伟,刘桂芬.Bayesian正规化BP神经网络及其在医学预测中的应用[J].2007中国卫生统计,2007,24(6):597-599.
[23]宋昕,汪晋宽,韩英华.东北大学信息科学与工程学院,基于Bayesian方法的鲁棒约束LMS算法[J].信息与控制,2007,36(5):73-79.
[24]George B M,Roger G M.The impact of the MIT-BIH Arrhythmia Database[J].IEEE Engineering in Medicine and Biology,2001,39(7):45-50.
[25]Sabah M A,Anwer S,Mohammed A Z.ECG data analysis using optimal non-orthogonal wavelet transform[J].Medical Engineering and Physics,2000,11(1):39-46.
[26]Lovell.A System for Real-Time Gesture Recognition and Classification of Coordinated Motion[D].D.Thesis,MIT EECS Dept.,Jan.2005.
[27]Paradiso,J.A..Interactive Therapy with Instrumented Footwear[C].Proceeding of the ACM Conference on Human Factors and Computing Systems,Austria,2004:341-343.
[28]王建军,陈日新.数字信号处理在医学上的应用[J].计算机技术与自动化,2000:19(2):45-47.
[29]刘少颖,卢继来.基于数学形态学和小波分解的QRS波群检测算法[J].清华大学学报,2004:44(6):852-855.
[30]傅景广,许刚.基于遗传算法地聚类分析[J].计算机工程,2004,30(4):122-124.
[31]边肇祺,张学工.模式识别[M].北京:清华大学出版社,2000.
[32]张学工译.统计学习理论的本质[M].北京:清华大学出版社,2000.
[33]薛毅.最优化原理与方法[M].北京:北京工业大学出版社,2001.
[34]Fabio C F,Jay G..NET数据服务C#高级编程[M].北京:清华大学出版社,2002.
[35]郑阿奇,梁敬东,朱毅华等.C#实用教程[M].北京:电子工业出版社,2008.
[36]Adrian Kingsley-Hughes Kathie Kingsley-Hughes,施宏斌(译).C#2005编程进阶与参考手册[M].北京:清华大学出版社,2007.
[37]刘新军,盛泉,李辛鹤译..NET精简框架程序设计C#版[M].北京:电子工业出版社,2006.
[38]杨宏桥,吴飞,甘仞初.构建区域协同医疗信息系统的设计方案研究[J].医疗卫生装备,2008,29(5):50-52.
[39]赵莹,杨阳,王志云.Microsoft.NET Compact Framework 2.0中数据访问模型解析[J].软件开发与应用,2007,10(26):95-96.
[40]赵醍,张跃,周炳坤.医用PDA心电实时监护软件系统的设计与实现[J].计算机应用,2006,26(12):339-341.
[2]俞梦孙.关于我国医学信息技术的发展[J].中国生物医学工程学报,2008,27(2):161-163.
[3]Benny lo.Body Sensor Networks-research challenges and opportunities[C].Proceedings of the international Workshop on Wearable and implantable Body Sensor Networks,London,2006:7-15.
[4]Omer Aziz,Benny lo.An Approach to Monitoring the Post-operative Surgical Patient[C].Proceedings of the international Workshop on Wearable and implant table Body Sensor Networks,London,2006:41-47.
[5]许剑,卢建刚.多参数无线医疗监护系统的设计与开发[J].中国医疗器械杂志,2005,29(6):406-409.
[6]胡广书.现代信号处理教程[M].北京:清华大学出版社,2004.
[7]Borkar S.Design challenges of technology scaling[C].IEEE Micro,1999,19(4):23-29.
[8]Culler S,Estrin D.Overview of sensor networks[C].Computer 2004,37(8):41-49.
[9]Warneke B,last M,Pister KSJ.Smart dust:communicating with a cubic-millimeter[A].Computer 2001,34(1):44-51.
[10]林媛,陈新.面向对象的医疗诊断推理机设计[J].计算机应用与软件,2001,18(1):5-9.
[11]王希军,程严.远程医疗诊断现状[M].北京:人民邮电出版社,2005.
[12]Izawa KP,YamadaS,OkaKetal.Long-term exercise maintenance physical activity and health-related quality of life after cardiac rehabilitation[C].International Workshop on Wearable and Implantable Body Sensor Networks,London,2005:131-135.
[13]Lo BPL,Yang GZ.Key Technical Challenges and Current Implementations of Body Sensor Networks[C].International Workshop on Wearable and Implantable Body Sensor Networks,London,2005:71-77.
[14]Julien Pansiot,Benny Lo,Guang-Zhong Yang.Towards Image-Based Modeling for Ambient Sensing[C].Proceedings of the International Workshop on Wearable and Implantable Body Sensor Networks,London,2006:83-89.
[15]Surapa Thiemjars,Benny Lo,Guang-Zhong Yang.A Spatio-Temporal Architecture for Context Aware Sensing[C].Proceedings of the International Workshop on Wearable and Implantable Body Sensor Networks,London,2006:171-176.
[16]Surapa Thiemjarus,Rachel King,Benny Lo.A noise resilient distributed inference framework for Body sensor Networks[C].International Workshop on Wearable and Implantable Body Sensor Networks,London,2006:101-107.
[17]BennyLo,Louis,AraDarzi.Real-Time Pervasive Monitoring for Postoperative Care[C].Proceedings of the International Workshop on Wearable and Implantable Body Sensor Networks,London,2006:156-161.
[18]Benny Lo,Guang-Zhong Yang.The perspective of Body sensor Networks[C].Proceedings of the International Workshop on Wearable and Implantable Body Sensor Networks,London,2006:91-96.
[19]An Introduction to Bayesian Networks and their Contemporary Applications,reference:http://www.niedermayer.ca/index.html.
[20]徐从富.浙江大学计算机科学学院.《人工智能》 课件:第七章,贝叶斯网络,54-97.
[21]冀俊忠,刘椿年,阎静.一种快速的贝叶斯网结构学习算法[M].北京:北京工业大学出版社,2007.
[22]徐建伟,刘桂芬.Bayesian正规化BP神经网络及其在医学预测中的应用[J].2007中国卫生统计,2007,24(6):597-599.
[23]宋昕,汪晋宽,韩英华.东北大学信息科学与工程学院,基于Bayesian方法的鲁棒约束LMS算法[J].信息与控制,2007,36(5):73-79.
[24]George B M,Roger G M.The impact of the MIT-BIH Arrhythmia Database[J].IEEE Engineering in Medicine and Biology,2001,39(7):45-50.
[25]Sabah M A,Anwer S,Mohammed A Z.ECG data analysis using optimal non-orthogonal wavelet transform[J].Medical Engineering and Physics,2000,11(1):39-46.
[26]Lovell.A System for Real-Time Gesture Recognition and Classification of Coordinated Motion[D].D.Thesis,MIT EECS Dept.,Jan.2005.
[27]Paradiso,J.A..Interactive Therapy with Instrumented Footwear[C].Proceeding of the ACM Conference on Human Factors and Computing Systems,Austria,2004:341-343.
[28]王建军,陈日新.数字信号处理在医学上的应用[J].计算机技术与自动化,2000:19(2):45-47.
[29]刘少颖,卢继来.基于数学形态学和小波分解的QRS波群检测算法[J].清华大学学报,2004:44(6):852-855.
[30]傅景广,许刚.基于遗传算法地聚类分析[J].计算机工程,2004,30(4):122-124.
[31]边肇祺,张学工.模式识别[M].北京:清华大学出版社,2000.
[32]张学工译.统计学习理论的本质[M].北京:清华大学出版社,2000.
[33]薛毅.最优化原理与方法[M].北京:北京工业大学出版社,2001.
[34]Fabio C F,Jay G..NET数据服务C#高级编程[M].北京:清华大学出版社,2002.
[35]郑阿奇,梁敬东,朱毅华等.C#实用教程[M].北京:电子工业出版社,2008.
[36]Adrian Kingsley-Hughes Kathie Kingsley-Hughes,施宏斌(译).C#2005编程进阶与参考手册[M].北京:清华大学出版社,2007.
[37]刘新军,盛泉,李辛鹤译..NET精简框架程序设计C#版[M].北京:电子工业出版社,2006.
[38]杨宏桥,吴飞,甘仞初.构建区域协同医疗信息系统的设计方案研究[J].医疗卫生装备,2008,29(5):50-52.
[39]赵莹,杨阳,王志云.Microsoft.NET Compact Framework 2.0中数据访问模型解析[J].软件开发与应用,2007,10(26):95-96.
[40]赵醍,张跃,周炳坤.医用PDA心电实时监护软件系统的设计与实现[J].计算机应用,2006,26(12):339-341.