基于智能手机的跌倒行为识别算法研究
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摘要
利用智能手机的感知和计算能力,对跌倒行为识别算法进行研究。分析使用多特征传感器识别跌倒行为的必要性,并根据传感器对跌倒行为敏感程度的不同,提出基于主成分分析的多特征选择方法。针对传统K-means算法不能反映数据分布差异的问题,设计基于相对熵的跌倒行为识别算法,利用数据集分布距离进行跌倒行为识别。在真实环境下采集跌倒行为数据对算法性能进行评估,结果表明该算法能较好地识别跌倒行为,识别准确率高达96. 7%。
This paper uses the perception and computing capacity of smartphones to study the fall behavior recognition algorithm. It analyzes the necessity of using multi-feature sensors to detect the fall behavior. According to the sensitivity of various sensors to the fall behavior,a multi-features selection method based on Principal Component Analysis(PCA) is proposed. Aiming at the problem that traditional K-means algorithm can not reflect the difference of distribution of data,a relative entropy based behavior identification algorithm is proposed. It uses the distance of distribution of data for fall behavior detection. It collects the data of the fall behavior under realistic environments and evaluates the performance of the proposed algorithm. Results show that the proposed algorithm can effectively detect fall behaviors and the accuracy is up to 96. 7%.
This paper uses the perception and computing capacity of smartphones to study the fall behavior recognition algorithm. It analyzes the necessity of using multi-feature sensors to detect the fall behavior. According to the sensitivity of various sensors to the fall behavior,a multi-features selection method based on Principal Component Analysis(PCA) is proposed. Aiming at the problem that traditional K-means algorithm can not reflect the difference of distribution of data,a relative entropy based behavior identification algorithm is proposed. It uses the distance of distribution of data for fall behavior detection. It collects the data of the fall behavior under realistic environments and evaluates the performance of the proposed algorithm. Results show that the proposed algorithm can effectively detect fall behaviors and the accuracy is up to 96. 7%.
引文
[1]毛立昱.基于手机的跌倒监测系统设计与实现[D].成都:电子科技大学,2014.
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[10]秦昉,孙子文,白勇.基于智能手机的实时跌倒检测系统研究[J].计算机工程与应用,2016,52(11):136-140.
[11]孙晓雯,孙子文,秦昉.基于阈值与PSO-SVM的人体跌倒检测研究[J].计算机工程,2016,42(5):317-321.
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[14]SPOSARO F,TYSON G.Ifall:an android application for fall monitoring and response[C]//Proceedings of 2009Annual International Conference of the IEEE Engineering in Medicine and Biology Society.Washington D.C.,USA:IEEE Press,2009:6119-6122.
[15]YIN J,QIANG Y,PAN J J.Sensor-based abnormal humanactivity detection[J].IEEE Transactions on Knowledge and Data Engineering,2008,20(8):1082-1090.
[16]何山.基于智能手机的日常活动识别和跌倒检测[D].武汉:华中科技大学,2013.
[17]BRO R,SMILDE A K.Principal component analysis[J].Analytical Methods,2014,6(9):2812-2831.
[18]王松,夏绍玮.一种鲁棒主成分分析(PCA)算法[J].系统工程理论与实践,1998(1):10-14.
[19]吴艳文,胡学钢.一种K-means算法的k值优化方案[J].巢湖学院学报,2007,9(6):21-24.
[20]HERSHEY J R,OLSEN P A.Approximating the Kullback-Leibler divergence between Gaussian mixture models[C]//Proceedings of IEEE International Conference on Acoustics,Speech and Signal Processing.Washington D.C.,USA:IEEE Press,2007:15-20.
[21]相对熵[EB/OL].[2017-08-11].https://zh.wikipedia.org/zh-hans/%E7%9B%B8%E5%AF%B9%E7%86%B5.
[22]PARZEN E.On estimation of a probability density function and mode[J].Journal Annals of Mathematical Statistics,1962,33(3):1065-1076.
[2]周敏.基于移动终端的跌倒检测方法研究[D].湘潭:湘潭大学,2013.
[3]MADANSINGH S,THRASHER T A,LAYNE C S,et al.Smartphone based fall detection system[C]//Proceedings of the 15th International Conference on Control,Automation and Systems.Washington D.C.,USA:IEEE Press,2015:370-374.
[4]秦昉.基于传感器的跌倒检测系统的研究与实现[D].无锡:江南大学,2015.
[5]MCKENNA J,CHARIF N.Summarising contextual activity and detecting unusual inactivity in a supportive home environment[J].Pattern Analysis and Applications,2004,7(4):386-401.
[6]ALWAN M,RAJENDRAN P J,KELL S,et al.A smart and passive floor-vibration based fall detector for elderly[C]//Proceedings of the 2nd International Conference on Information and Communication Technologies.Washington D.C.,USA:IEEE Press,2006:1-8.
[7]BAEK W S,KIM D M,BASHIR F,et al.Real life applicable fall detection system based on wireless body area network[C]//Proceedings of Consumer Communications and Networking Conference.Washington D.C.,USA:IEEE Press,2013:62-67.
[8]CHEN B,GWIN J.Fall detection and risk of falling assessment with wearable sensors[C]//Proceedings of Conference on Wireless Health.New York,USA:ACM Press,2012:13-17.
[9]全球智能手机用户数量分析[EB/OL].[2017-08-11].http://m.53shop.com/news/kes47743.html.
[10]秦昉,孙子文,白勇.基于智能手机的实时跌倒检测系统研究[J].计算机工程与应用,2016,52(11):136-140.
[11]孙晓雯,孙子文,秦昉.基于阈值与PSO-SVM的人体跌倒检测研究[J].计算机工程,2016,42(5):317-321.
[12]LAI C F,HUANG Y M,PARK J H,et al.Adaptive body posture analysis for elderly-falling detection with multisensors[J].IEEE Intelligent Systems,2010,25(2):20-30.
[13]CASILARI E,OVIEDO-JIMNEZ M A.Automatic fall detection system based on the combined use of a smartphone and a smartwatch[J].PLOS One,2015,10(11).
[14]SPOSARO F,TYSON G.Ifall:an android application for fall monitoring and response[C]//Proceedings of 2009Annual International Conference of the IEEE Engineering in Medicine and Biology Society.Washington D.C.,USA:IEEE Press,2009:6119-6122.
[15]YIN J,QIANG Y,PAN J J.Sensor-based abnormal humanactivity detection[J].IEEE Transactions on Knowledge and Data Engineering,2008,20(8):1082-1090.
[16]何山.基于智能手机的日常活动识别和跌倒检测[D].武汉:华中科技大学,2013.
[17]BRO R,SMILDE A K.Principal component analysis[J].Analytical Methods,2014,6(9):2812-2831.
[18]王松,夏绍玮.一种鲁棒主成分分析(PCA)算法[J].系统工程理论与实践,1998(1):10-14.
[19]吴艳文,胡学钢.一种K-means算法的k值优化方案[J].巢湖学院学报,2007,9(6):21-24.
[20]HERSHEY J R,OLSEN P A.Approximating the Kullback-Leibler divergence between Gaussian mixture models[C]//Proceedings of IEEE International Conference on Acoustics,Speech and Signal Processing.Washington D.C.,USA:IEEE Press,2007:15-20.
[21]相对熵[EB/OL].[2017-08-11].https://zh.wikipedia.org/zh-hans/%E7%9B%B8%E5%AF%B9%E7%86%B5.
[22]PARZEN E.On estimation of a probability density function and mode[J].Journal Annals of Mathematical Statistics,1962,33(3):1065-1076.