基于无线信道状态信息的跌倒无源监测方法
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摘要
针对传统基于视频或传感器的跌倒检测方法中环境依赖、空间受限等问题,提出了一种基于无线信道状态信息的跌倒无源检测方法Fallsense。该方法利用普适、低成本的商用WiFi设备,首先采集无线信道状态数据并对数据进行预处理,然后设计动作—信号分析模型,建立轻量级动态模板匹配算法以从时序信道数据中实时检测出承载真实跌倒事件的相关片段。大量实际环境下的实验表明, Fallsense可以实现较高的准确率以及较低的误报率,准确率达到95%,误报率为2.44%。与经典WiFall系统相比,Fallsense将时间复杂度从WiFall的O(mN log N)降低到O(N)(N是样本数,m是特征数),且准确率提高了2.69%,误报率下降了4.66%。实验结果表明,所提方法是一种快速高效的无源跌倒检测方法。
Traditional vision-based or sensor-based falling detection systems possess certain inherent shortcomings such as hardware dependence and coverage limitation, hence Fallsense, a passive falling detection method based on wireless Channel State Information(CSI) was proposed. The method was based on low-cost, pervasive and commercial WiFi devices. Firstly, the wireless CSI data was collected and preprocessed. Then a model of motion-signal analysis was built, where a lightweight dynamic template matching algorithm was designed to detect relevant fragments of real falling events from the time-series channel data in real time. Experiments in a large number of actual environments show that Fallsense can achieve high accuracy and low false positive rate, with an accuracy of 95% and a false positive rate of 2.44%. Compared with the classic WiFall system, Fallsense reduces the time complexity from O(mN log N) to O(N)(N is the sample number, m is the feature number), and increases the accuracy by 2.69%, decreases the false positive rate by 4.66%. The experimental results confirm that this passive falling detection method is fast and efficient.
Traditional vision-based or sensor-based falling detection systems possess certain inherent shortcomings such as hardware dependence and coverage limitation, hence Fallsense, a passive falling detection method based on wireless Channel State Information(CSI) was proposed. The method was based on low-cost, pervasive and commercial WiFi devices. Firstly, the wireless CSI data was collected and preprocessed. Then a model of motion-signal analysis was built, where a lightweight dynamic template matching algorithm was designed to detect relevant fragments of real falling events from the time-series channel data in real time. Experiments in a large number of actual environments show that Fallsense can achieve high accuracy and low false positive rate, with an accuracy of 95% and a false positive rate of 2.44%. Compared with the classic WiFall system, Fallsense reduces the time complexity from O(mN log N) to O(N)(N is the sample number, m is the feature number), and increases the accuracy by 2.69%, decreases the false positive rate by 4.66%. The experimental results confirm that this passive falling detection method is fast and efficient.
引文
[1] WHO.Falls [EB/OL].[2018- 01- 16].https://www.who.int/news-room/fact-sheets/detail/falls.
[2] ZHANG Z,CONLY C,ATHITSOS V.A survey on vision-based fall detection[C]// Proceedings of the 8th ACM International Conference on PErvasive Technologies Related to Assistive Environments.New York:ACM,2015:Article No.46.
[3] RITA C,ANDREA P,ROBERTO V.A multi-camera vision system for fall detection and alarm generation[J].Expert Systems,2010,24(5):334-345.
[4] NUNEZ-MARCOS A,AZKUNE G,ARGANDA-CARRERAS I.Vision-based fall detection with convolutional neural networks[J].Wireless Communications & Mobile Computing,2017,2017:Article ID 9474806.
[5] 沈秉乾,武志勇,贺前华,等.人体姿势状态判决的跌倒检测方法[J].计算机应用,2014,34(S1):223-227.(SHEN B Q,WU Z Y,HE Q H,et al.Fall detection method for human posture state judgment[J].Journal of Computer Applications,2014,34(S1):223-227.)
[6] 李雷,张帆,施化吉,等.穿戴式跌倒检测中特征向量的提取和降维研究[J].计算机应用研究,2019,36(1):103-105.(LI L,ZHANG F,SHI H J,et al.Extraction and dimensionality reduction of feature vectors in wearable fall detection[J].Application Research of Computers,2019,36(1):103-105.)
[7] MOSTARAC P,MALARIC R,JURCEVIC M,et al.System for monitoring and fall detection of patients using mobile 3-axis accelerometers sensors[C]// Proceedings of the 2011 IEEE International Symposium on Medical Measurements and Applications.Piscataway,NJ:IEEE,2011:456-459.
[8] ZIGEL Y,LITVAK D,GANNOT I.A method for automatic fall detection of elderly people using floor vibrations and sound-proof of concept on human mimicking doll falls[J].IEEE Transactions on Biomedical Engineering,2009,56(12):2858-2867.
[9] 沈莹,黎海涛.基于移动终端的老年人跌倒检测系统设计[J].中国新通信,2016,18(7):13-15.(SHEN Y,LI H T.Design of elderly fall detection system based on mobile terminal [J].China New Telecommunication,2016,18(7):13-15.)
[10] 王忠民,张新平,梁琛.一种基于时序分析异常数据的跌倒行为监测方法[J].计算机应用研究,2018,35(3):839-843.(WANG Z M,ZHANG X P,LIANG C.A fall behavior monitoring method based on time series analysis abnormal data [J].Application Research of Computers,2018,35(3):839-843.)
[11] KIANOUSH S,SAVAZZI S,VICENTINI F,et al.Leveraging RF signals for human sensing:fall detection and localization in human-machine shared workspaces[C]// Proceedings of the 2015 IEEE 13th International Conference on Industrial Informatics.Piscataway,NJ:IEEE,2015:1456-1462.
[12] KIANOUSH S,SAVAZZI S,VICENTINI F,et al.Device-free RF human body fall detection and localization in industrial workplaces[J].IEEE Internet of Things Journal,2017,4(2):351-362.
[13] HAN C,WU K,WANG Y,et al.Wifall:device-free fall detection by wireless networks[C]// INFOCOM 2014:Proceedings of the 2014 IEEE Conference on Computer Communicationsns.Piscataway,NJ:IEEE,2014:271-279.
[14] WANG Y,WU K,NI L M.WiFall:Device-free fall detection by wireless networks[J].IEEE Transactions on Mobile Computing,2017,16(2):581-594.
[15] ZHANG D,WANG H,WANG Y,et al.Anti-fall:a non-intrusive and real-time fall detector leveraging CSI from commodity WiFi devices[C]// ICOST 2015:Proceedings of the 2015 International Conference on Smart Homes and Health Telematics.Berlin:Springer,2015:181-193.
[16] WANG H,ZHANG D,WANG Y,et al.RT-fall:a real-time and contactless fall detection system with commodity WiFi devices[J].IEEE Transactions on Mobile Computing,2017,16(2):511-526.
[17] GU Y,ZHAN J,JI Y,et al.MoSense:a RF-based motion detection system via off-the-shelf WiFi devices[J].IEEE Internet of Things Journal,2017,4(6):2326-2341.
[18] GU Y,LIU T,LI J,et al.EmoSense:data-driven emotion sensing via off-the-shelf WiFi devices[C]// Proceedings of the 2018 IEEE International Conference on Communications.Piscataway,NJ:IEEE,2018:1-6.
[2] ZHANG Z,CONLY C,ATHITSOS V.A survey on vision-based fall detection[C]// Proceedings of the 8th ACM International Conference on PErvasive Technologies Related to Assistive Environments.New York:ACM,2015:Article No.46.
[3] RITA C,ANDREA P,ROBERTO V.A multi-camera vision system for fall detection and alarm generation[J].Expert Systems,2010,24(5):334-345.
[4] NUNEZ-MARCOS A,AZKUNE G,ARGANDA-CARRERAS I.Vision-based fall detection with convolutional neural networks[J].Wireless Communications & Mobile Computing,2017,2017:Article ID 9474806.
[5] 沈秉乾,武志勇,贺前华,等.人体姿势状态判决的跌倒检测方法[J].计算机应用,2014,34(S1):223-227.(SHEN B Q,WU Z Y,HE Q H,et al.Fall detection method for human posture state judgment[J].Journal of Computer Applications,2014,34(S1):223-227.)
[6] 李雷,张帆,施化吉,等.穿戴式跌倒检测中特征向量的提取和降维研究[J].计算机应用研究,2019,36(1):103-105.(LI L,ZHANG F,SHI H J,et al.Extraction and dimensionality reduction of feature vectors in wearable fall detection[J].Application Research of Computers,2019,36(1):103-105.)
[7] MOSTARAC P,MALARIC R,JURCEVIC M,et al.System for monitoring and fall detection of patients using mobile 3-axis accelerometers sensors[C]// Proceedings of the 2011 IEEE International Symposium on Medical Measurements and Applications.Piscataway,NJ:IEEE,2011:456-459.
[8] ZIGEL Y,LITVAK D,GANNOT I.A method for automatic fall detection of elderly people using floor vibrations and sound-proof of concept on human mimicking doll falls[J].IEEE Transactions on Biomedical Engineering,2009,56(12):2858-2867.
[9] 沈莹,黎海涛.基于移动终端的老年人跌倒检测系统设计[J].中国新通信,2016,18(7):13-15.(SHEN Y,LI H T.Design of elderly fall detection system based on mobile terminal [J].China New Telecommunication,2016,18(7):13-15.)
[10] 王忠民,张新平,梁琛.一种基于时序分析异常数据的跌倒行为监测方法[J].计算机应用研究,2018,35(3):839-843.(WANG Z M,ZHANG X P,LIANG C.A fall behavior monitoring method based on time series analysis abnormal data [J].Application Research of Computers,2018,35(3):839-843.)
[11] KIANOUSH S,SAVAZZI S,VICENTINI F,et al.Leveraging RF signals for human sensing:fall detection and localization in human-machine shared workspaces[C]// Proceedings of the 2015 IEEE 13th International Conference on Industrial Informatics.Piscataway,NJ:IEEE,2015:1456-1462.
[12] KIANOUSH S,SAVAZZI S,VICENTINI F,et al.Device-free RF human body fall detection and localization in industrial workplaces[J].IEEE Internet of Things Journal,2017,4(2):351-362.
[13] HAN C,WU K,WANG Y,et al.Wifall:device-free fall detection by wireless networks[C]// INFOCOM 2014:Proceedings of the 2014 IEEE Conference on Computer Communicationsns.Piscataway,NJ:IEEE,2014:271-279.
[14] WANG Y,WU K,NI L M.WiFall:Device-free fall detection by wireless networks[J].IEEE Transactions on Mobile Computing,2017,16(2):581-594.
[15] ZHANG D,WANG H,WANG Y,et al.Anti-fall:a non-intrusive and real-time fall detector leveraging CSI from commodity WiFi devices[C]// ICOST 2015:Proceedings of the 2015 International Conference on Smart Homes and Health Telematics.Berlin:Springer,2015:181-193.
[16] WANG H,ZHANG D,WANG Y,et al.RT-fall:a real-time and contactless fall detection system with commodity WiFi devices[J].IEEE Transactions on Mobile Computing,2017,16(2):511-526.
[17] GU Y,ZHAN J,JI Y,et al.MoSense:a RF-based motion detection system via off-the-shelf WiFi devices[J].IEEE Internet of Things Journal,2017,4(6):2326-2341.
[18] GU Y,LIU T,LI J,et al.EmoSense:data-driven emotion sensing via off-the-shelf WiFi devices[C]// Proceedings of the 2018 IEEE International Conference on Communications.Piscataway,NJ:IEEE,2018:1-6.