老年人跌倒检测原型系统设计与实现
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摘要
世界面临着老龄化的严峻挑战,整个国际社会都在关注老龄化问题。跌倒是老年人常发生的意外,严重影响了老年人的身心健康。
针对老年人身体平衡的特点,结合微传感器、数字信号处理以及无线传感器网络等技术,利用人体运动产生的加速度,设计了一种基于加速度传感器的跌倒检测系统以及相应的跌倒算法,可以为老年人提供安全保障与准确的监测,同时保护用户完全的隐私。对独居老年人的日常生活状态实施远程监护,不仅能提高医护人员的救助效率和老人的生活质量,还可以评估监护对象的独立生活能力和健康状况,也可以老人在熟悉的环境(家)中得到及时的救护,减小老人的心理压力,减少跌倒发生的可能性。
本论文以重庆市自然科学基金”基于无线传感器网络的跌倒检测机制研究”(cstc 2007bb2194)为选题背景,在介绍无线传感器网络概念与应用的基础上,研究和探讨了将无线传感器网络运用到医疗监护领域的背景和可行性,重点研究了跌倒检测节点的硬件设计,无线通信和跌倒算法的软件实现以及后台监控系统,并对整体设计进行了实验调试。利用Internet网络和RF射频实现加速度值传输以及诊断信息反馈,后台监控软件以.NET为开发平台进行模块化设计,通过数据通信模块、跌倒检测与诊断模块及数据库管理模块实现了远程加速度数据的传输、回放和人体状态的快速分析诊断以及医患之间的信息交互,最大限度的减少由跌倒引发意外伤亡。该系统集成度高、体积小、智能化、可靠性强,具有不受患者活动状态、活动时间限制等优点。
在人体佩戴实验与分析中,该装置可以区分出正常人体活动与跌倒事件,自动发出跌倒报警信号,从而使用户在尽可能短的时间内得到医疗救护。
The international communities as a whole pay more and more attentions on aging population while entering aging periods. Fall is one of the common accident happened in elderly people, causing serious damage physically as well as psychologically.
A fall detecting system and relevant algorithm is designed based on accelerometer, which is being developed to safely and accurately monitor the user while still allowing complete privacy and security. It is combined with micro-sensors, digital signal process and wireless sensor network (WSN) technologies together, weighing the characteristic of elderly people’s body balance. The implementation of remote monitor on daily life status of elderly people who live alone, could significantly improve the aid efficiency and life quality of the old, assessing the capability to live alone and healthy status, allowing user to stay at familiar environment and receive necessary help in time which could reduce the distress of user so as to cut down the occurrence of fall.
This paper stems from the Chongqing National Science Foundation project,“Research on fall detection mechanism base on WSN”(cstc 2007bb2194). The concept and application of WSN is being introduced in this thesis, followed by the background and possibility of applying WSN on medical monitor are discussed and researched. It focuses on the hardware design of fall detection node, wireless transmitting and software realization of fall detecting algorithm as well as backstage monitor system. Utilizing Internet and RF to transmit acceleration value and diagnostic information feedback, the backstage monitoring software is developed with .NET platform in a modularization way. Through data communication, fall detection and diagnosing and database managing module, the transmission, flashback and prompt analysis of remote acceleration data are implemented along with interreact between doctor and user, minimizing death ratio caused by accidental fall. The system designed in this paper is highly integrated, small sized, intelligent and solidly reliable, without confinement of users’active status or time etc.
As field experiment shows that this detection system can distinguish fall event from normal activities, and raise fall alarm automatically to get a shortened interval of aid.
针对老年人身体平衡的特点,结合微传感器、数字信号处理以及无线传感器网络等技术,利用人体运动产生的加速度,设计了一种基于加速度传感器的跌倒检测系统以及相应的跌倒算法,可以为老年人提供安全保障与准确的监测,同时保护用户完全的隐私。对独居老年人的日常生活状态实施远程监护,不仅能提高医护人员的救助效率和老人的生活质量,还可以评估监护对象的独立生活能力和健康状况,也可以老人在熟悉的环境(家)中得到及时的救护,减小老人的心理压力,减少跌倒发生的可能性。
本论文以重庆市自然科学基金”基于无线传感器网络的跌倒检测机制研究”(cstc 2007bb2194)为选题背景,在介绍无线传感器网络概念与应用的基础上,研究和探讨了将无线传感器网络运用到医疗监护领域的背景和可行性,重点研究了跌倒检测节点的硬件设计,无线通信和跌倒算法的软件实现以及后台监控系统,并对整体设计进行了实验调试。利用Internet网络和RF射频实现加速度值传输以及诊断信息反馈,后台监控软件以.NET为开发平台进行模块化设计,通过数据通信模块、跌倒检测与诊断模块及数据库管理模块实现了远程加速度数据的传输、回放和人体状态的快速分析诊断以及医患之间的信息交互,最大限度的减少由跌倒引发意外伤亡。该系统集成度高、体积小、智能化、可靠性强,具有不受患者活动状态、活动时间限制等优点。
在人体佩戴实验与分析中,该装置可以区分出正常人体活动与跌倒事件,自动发出跌倒报警信号,从而使用户在尽可能短的时间内得到医疗救护。
The international communities as a whole pay more and more attentions on aging population while entering aging periods. Fall is one of the common accident happened in elderly people, causing serious damage physically as well as psychologically.
A fall detecting system and relevant algorithm is designed based on accelerometer, which is being developed to safely and accurately monitor the user while still allowing complete privacy and security. It is combined with micro-sensors, digital signal process and wireless sensor network (WSN) technologies together, weighing the characteristic of elderly people’s body balance. The implementation of remote monitor on daily life status of elderly people who live alone, could significantly improve the aid efficiency and life quality of the old, assessing the capability to live alone and healthy status, allowing user to stay at familiar environment and receive necessary help in time which could reduce the distress of user so as to cut down the occurrence of fall.
This paper stems from the Chongqing National Science Foundation project,“Research on fall detection mechanism base on WSN”(cstc 2007bb2194). The concept and application of WSN is being introduced in this thesis, followed by the background and possibility of applying WSN on medical monitor are discussed and researched. It focuses on the hardware design of fall detection node, wireless transmitting and software realization of fall detecting algorithm as well as backstage monitor system. Utilizing Internet and RF to transmit acceleration value and diagnostic information feedback, the backstage monitoring software is developed with .NET platform in a modularization way. Through data communication, fall detection and diagnosing and database managing module, the transmission, flashback and prompt analysis of remote acceleration data are implemented along with interreact between doctor and user, minimizing death ratio caused by accidental fall. The system designed in this paper is highly integrated, small sized, intelligent and solidly reliable, without confinement of users’active status or time etc.
As field experiment shows that this detection system can distinguish fall event from normal activities, and raise fall alarm automatically to get a shortened interval of aid.
引文
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[9] Seon-Woo Lee, Kenji Mase,”Activity and location recognition using wearable sensors”[J], Pervasive Computing, 2002. 24-32P.
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[15] Suhuai Luo and Qingmao Hu, A Dynamic Motion Pattern Analysis Approach to Fall Detection, [C] ,IEEE International Workshop on Biomedical Circuits & Systems 2004:53 -56P.
[16] Jay Chen, Karric Kwong, Dennis Chang, Jerry Luk, Ruzena Bajcsy, Wearable Sensors for Reliable Fall Detection, [C] ,Proceedings of the 2005 IEEE Engineering in Medicine and Biology 27th Annual Conference, Shanghai, China, September 1-4, 2005:1203-1209P.
[17]陈协庆,陈嘉玲,李国荣,“Assistive technology device-development of a fall-detecting and alarm device”[R] ,行政院国家科学委员会专题研究计划成果报告, NSC 89-2213-E-324-051,2001.
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[31]于海斌,曾鹏,智能无线传感器网络系统[M],2006,科学出版社.
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[33]孙利民,李建中,陈渝,无线传感器网络[M];清华大学出版社,2005.
[34]李林涛,王声涌,荆春霞.老年跌倒的危险因素研究[J].疾病控制杂志,2001;5:227-229页.
[35]王声踊.我国伤害预防与控制研究现况和展望[J].中华流行病学杂志,2000;21:375-377页.
[36] Connell BR, Wolf SL. Environmental and behavioral circumstances associated with falls at home among healthy elderly individuals. [J], Arch Phys Med Rehabil,1997 ;78:179-186P.
[37] Tromp AM, Pluijm SM, Smit JH, Deeg DJ, Bouter LM, Lips P. Fall-risk screening test: a prospective study on predictors for falls in community-dwelling elderly. [J], Clinic Epidemiol, 2001;54(8):837-844P.
[38] B.Awerbuch, A.V. Goldberg, M.Luby, and S. A. Plotkin, "Network decomposition and locality in distributed computation,”[C], Proceedings of the IEEE 30th Annual Symposium on Foundations of Computer Science, Research Triangle Park, NC, USA, , 30 October一1 November .1989. 1:364-369P.
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[2] Gibson MJ, Andres RO, Isaacs B, et al. The prevention of falls in later life. A report of the Kellogg International Work Group on the prevention of falls by the elderly [J]. Danish Med Bull, 1987, 34: 1-24P.
[3]北京协和医院世界卫生组织疾病分类合作中心[M],编译.疾病和有关健康问题的国际统计分类ICD2101第10版,北京:人民卫生出版社,1996 ,3 :839-841页.
[4] Hendrie D, Hall SE, Arena G, et al. Health system costs of falls of older adults in Western Australia [R]. Aust Health Rev, 2004, 28: 363-373P.
[5] Rizzo JA, Friedkin R, Williams CS, et al1 Health care utilization and costs in medicare population by fall status [R] Medical Care, 1998, 36 :1174-1188P.
[6] J. Pynoos and D. Sabata,“Home Environmental Modification: Preventing Falls at Home,”[C], International Conference of Aging, Disability and Independence, 2003. 169–174P.
[7]吕筠.老年伤害研究回顾[J],疾病控制杂志,1999 ,3 :300-303页.
[8] N. M. Barnes, N. H. Edwards, D. A. D. Rose, and P. Garner.Lifestyle monitoring: technology for supported independence [J],IEE Computing and Control Engineering Journal, August 1998 :520-526P.
[9] Seon-Woo Lee, Kenji Mase,”Activity and location recognition using wearable sensors”[J], Pervasive Computing, 2002. 24-32P.
[10] Emil Jovanov, Amanda O’Donnell Lords, Dejan Raskovic, Paul G. Cox, Reza Adhami, Frank Andrasik,”Stress monitoring using a distributed wireless intelligent sensor system”[J], IEEE Engineering in Medicine and Biology Magazine, May/June 2003. 49-55P.
[11] M. J. Fischer, N. A. Lynch, and M. S. Paterson,“Impossibility of distributed consensus with one faulty processor [J], Journal of the ACM, 1985. 32 (2):374-382P.
[12] P. Fraigniaud, A. Pelc, D. Peleg, and S. Perennes, "Assigning labels in unknown anonymous networks”[C] Proceedings of the 19th Annual ACM SIGACT-SIGOPS symposium on Principles of Distributed Computing, Portland, OR, USA, July 2000. 1:101-111P.
[13] Celler,B.G.;Hesketh,T.;Earnshaw,W.;IIsar,E.,”An instrumentation system for the remote monitoring of changes in functional health status of elderly at home”[C], Proceedings of the16th Annual International Conference of the IEEE, Volume:2 ,3-6 Nov,1994:908-909P.
[14] P. A. Bromiley, P. Courtney, and N. A. Thacker. Design of a visual system for detecting natural events by the use of an independent visual estimate: A human fall detector. [C] ,In Empirical Evaluation Methods in Computer Vision. 2002:231-235P.
[15] Suhuai Luo and Qingmao Hu, A Dynamic Motion Pattern Analysis Approach to Fall Detection, [C] ,IEEE International Workshop on Biomedical Circuits & Systems 2004:53 -56P.
[16] Jay Chen, Karric Kwong, Dennis Chang, Jerry Luk, Ruzena Bajcsy, Wearable Sensors for Reliable Fall Detection, [C] ,Proceedings of the 2005 IEEE Engineering in Medicine and Biology 27th Annual Conference, Shanghai, China, September 1-4, 2005:1203-1209P.
[17]陈协庆,陈嘉玲,李国荣,“Assistive technology device-development of a fall-detecting and alarm device”[R] ,行政院国家科学委员会专题研究计划成果报告, NSC 89-2213-E-324-051,2001.
[18]詹晓龙,可携式生理讯号撷取及传输系统之研制─以Windows-CE PDA为平台[D],长庚大学,电机工程所硕士论文,2000.
[19]钟志裕,杨明兴,应用FPGA与远端技术之独居老人安全监测系统[D] ,成功大学,电机工程研究所硕士论文,2003.
[20] Jay Chen, Karric Kwong, Dennis Chang, Jerry Luk, Ruzena Bajcsy, Wearable Sensors for Reliable Fall Detection, [C] ,Proceedings of the 2005 IEEE Engineering in Medicine and Biology 27th Annual Conference, Shanghai, China, September 1-4, 2005:1203-1209P.
[21] R. Bajcsy, J. Chen, K. Kwong, D. Chang, and J. Luk, Fall detection using wireless sensor networks, [C] , in submitted to the 27th Annual International Conference of the EMBS, September 2005:759-765P.
[22] Doughty, K., and Cameron, K.,‘‘Primary and secondary sensing techniques for fall detection in the home”, [R],Proceedings of Hospital without Walls, City University, London, 1999:104-116P.
[23] M. N. Nyan; Francis E. H. TAY; M.Manimaran; K. H. W. Seah, Garment-based detection of falls and activities of daily living using 3-axis MEMS accelerometer,[J],Journal of Physics: Conference Series 34, 2006:1059–1067P.
[24] F Block, C Baum, .Energy Efficient Self-organizing Communication Protocols for Wireless Sensor Networks[C]. Communications for Network centric Operations: Creating the Information Force, IEEE, 2001. 362-366P.
[25] Burdin, J.Dunyak, J.Cohesion of wireless sensor networks with MIMO communications. [C] SoutheastCon, 2005. Proceedings. IEEE.8-10 April 2005:547–551P.
[26] Liyang Yu; Neng Wang; Xiaoqiao Meng. Real-time forest fire detection with wireless sensor networks.[J] Wireless Communications, Networking and Mobile Computing, 2005. Volume 2, 23-26 Sept. 2005:1214–1217P.
[27] Brownfield, M.; Yatharth Gupta; Davis, N. Wireless sensor network denial of sleep attack. 2005. [C] Proceedings from the Sixth Annual IEEE. 15-17 June 2005 :356–364P.
[28] Shijin Dai; Xiaorong Jing; Lemin Li; Research and analysis on routing protocols for wireless sensor network .Communications, Circuits and Systems, 2005.[C] Proceedings.2005 International Conference on.Volume 1, 27-30 May 2005 :407–411P.
[29] Yamasaki, K.; Ohtsuki, T.Design of energy-efficient wireless sensor networks with censoring, on-off, and censoring and on-off sensors based on mutual information. [C] Vehicular Technology Conference, 2005. VTC 2005-Spring. 2005 IEEE 61st.Volume 2, 30 May-1 June 2005: Vol. 2. 1312–1316P.
[30] Nadeem, T.; Ashok Agrawala. Performance of IEEE 802.11 based wireless sensor networks in noisy environments [C], Performance, Computing, and Communications Conference, 2005. IPCCC 2005. 24th IEEE International.7-9 April 2005:471–476P.
[31]于海斌,曾鹏,智能无线传感器网络系统[M],2006,科学出版社.
[32]郑增威,吴朝晖,金水祥,无线传感器网络及其应用[M],计算机科学,2003Vol.30 NO.10.
[33]孙利民,李建中,陈渝,无线传感器网络[M];清华大学出版社,2005.
[34]李林涛,王声涌,荆春霞.老年跌倒的危险因素研究[J].疾病控制杂志,2001;5:227-229页.
[35]王声踊.我国伤害预防与控制研究现况和展望[J].中华流行病学杂志,2000;21:375-377页.
[36] Connell BR, Wolf SL. Environmental and behavioral circumstances associated with falls at home among healthy elderly individuals. [J], Arch Phys Med Rehabil,1997 ;78:179-186P.
[37] Tromp AM, Pluijm SM, Smit JH, Deeg DJ, Bouter LM, Lips P. Fall-risk screening test: a prospective study on predictors for falls in community-dwelling elderly. [J], Clinic Epidemiol, 2001;54(8):837-844P.
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