基于腕部可穿戴设备的跌倒监护系统设计与实现
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摘要
随着社会老龄化加快,老年人或病人的人工监护成本越来越高,针对目前各种跌倒监测装置的不足,设计了一款基于腕部智能穿戴设备的生理体征采集、跌倒监测和报警系统,该装置通过集成的各种传感器采集腕部高度、加速度、角速度等体征信号,并采用合适的算法实现了跌倒检测报警功能;实验结果表明,通过佩戴文章设计的腕部智能穿戴设备可以较好地实现对跌倒行为和日常非跌倒行为的区分,在不影响佩戴者舒适度情况下行为检出率可达到98.5%。
With the accelerated aging of the society,the cost of manual monitoring for the elderly or patients is getting higher and higher.Aiming at the shortage of all kinds of fall monitoring devices,a system of physiological signal acquisition,fall monitoring and remote alarm based on the wrist smart wearer is designed.The device collects the wrist height,accelerates and angular velocity through the integrated sensors.,and designed appropriate algorithm to relized the fall detection and remote alarm functions.The experimental results show that the smart wearer designed by this article can distinguish between the fall behavior and the daily non fall behavior better,and the detection rate can reach 98.5% without affecting the wearer's comfort.
With the accelerated aging of the society,the cost of manual monitoring for the elderly or patients is getting higher and higher.Aiming at the shortage of all kinds of fall monitoring devices,a system of physiological signal acquisition,fall monitoring and remote alarm based on the wrist smart wearer is designed.The device collects the wrist height,accelerates and angular velocity through the integrated sensors.,and designed appropriate algorithm to relized the fall detection and remote alarm functions.The experimental results show that the smart wearer designed by this article can distinguish between the fall behavior and the daily non fall behavior better,and the detection rate can reach 98.5% without affecting the wearer's comfort.
引文
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[9]王君泽,朱小龙,瞿畅.基于Kinect骨架追踪的人体摔倒自动检测[J].上海交通大学学报,2015,49(9):1359-1365.
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[13]欧志球,戴坚锋,王铮.基于高斯模糊的摔倒自检测算法设计与应用[J].计算机测量与控制,2015,23(11):3867-3870.
[14]卓从彬,杨龙频,周林,等.基于MPU6050加速度传感器的跌倒检测与报警系统设计[J].电子器件,2015,38(4):821-825.
[15]罗丹,罗海勇.基于随机森林的跌倒检测算法[J].计算机应用,2015,35(11):3157-3160.
[16]裴利然,姜萍萍,颜国正.基于支持向量机的跌倒检测算法研究[J].光学精密工程,2017,25(15):182-187.
[17]涂亚庆,陈鹏,陈宝欣,等.基于离散特征的跌倒检测智能方法及应用[J].仪器仪表学报,2017,38(3):629-634.
[18]石栋,张克华,徐彪.独居老人云智能跌倒实时检测系统的开发[J].计算机工程与应用,2016,52(19):259-264.
[2]黄淑芳,陈月宝,林绍英,等.综合干预对跌倒老年患者活动水平及害怕跌倒心理的影响[J].中国老年学杂志,2014,34(13):3584-3586.
[3]王维念,胡兆燕,陈正龙,等.可穿戴式跌倒检测及防护装置的研究现状[J].北京生物医学工程,2017,36(3):323-329.
[4]高青,陈洪波,冯涛,等.老年人跌倒检测系统的研究现状与发展趋势[J].医疗卫生装备,2015,36(12):102-105.
[5]张小驰,陈天华,许继平,等.基于多传感器融合的摔倒检测算法的研究[J].计算机测量与控制,2015,23(6):2237-2240.
[6]彭玉青,高晴晴,刘楠楠,等.基于多特征融合的跌倒行为识别与研究[J].数据采集与处理,2016,31(5):890-902.
[7]毕盛,刘皓熙,闵华清,等.基于多传感器信息融合的仿人机器人跌倒检测及控制[J].华南理工大学学报(自然科学版),2017,45(1):95-101.
[8]瞿畅,孙杰,王君泽,等.基于Kinect体感传感器的老年人跌倒自动检测[J].传感技术学报,2016,29(3):378-383.
[9]王君泽,朱小龙,瞿畅.基于Kinect骨架追踪的人体摔倒自动检测[J].上海交通大学学报,2015,49(9):1359-1365.
[10]Isabel N F,Carlos L,Luia P,et al.Exploring smartphone sensors for fall detection[11]. Mobile User Experience,2016,5(1),1-17.
[11]Schwickert L,Becker C,Lindemann C,et al.Fall detection with body-worn sensors, a systematic review[J].Zeitschrift für Gerontologie und Geriatrie,2013,46(9):706-719.
[12]孙子文,孙晓雯.基于加速度传感器的人体跌倒检测方法[J].计算机工程与科学,2017,39(2):330-335.
[13]欧志球,戴坚锋,王铮.基于高斯模糊的摔倒自检测算法设计与应用[J].计算机测量与控制,2015,23(11):3867-3870.
[14]卓从彬,杨龙频,周林,等.基于MPU6050加速度传感器的跌倒检测与报警系统设计[J].电子器件,2015,38(4):821-825.
[15]罗丹,罗海勇.基于随机森林的跌倒检测算法[J].计算机应用,2015,35(11):3157-3160.
[16]裴利然,姜萍萍,颜国正.基于支持向量机的跌倒检测算法研究[J].光学精密工程,2017,25(15):182-187.
[17]涂亚庆,陈鹏,陈宝欣,等.基于离散特征的跌倒检测智能方法及应用[J].仪器仪表学报,2017,38(3):629-634.
[18]石栋,张克华,徐彪.独居老人云智能跌倒实时检测系统的开发[J].计算机工程与应用,2016,52(19):259-264.