基于阈值分析法的人体跌倒检测系统
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摘要
为了在老年人跌倒时及时发现并进行救助,设计了一个跌倒检测、定位报警求救的智能系统。系统使用STM32F103ZET6作为微处理器,采用ADXL345三轴加速度传感器采集数据,并用ATK—NEO—6M实现全球定位,跌倒发生时,SIM900A发送包含位置信息的求救短信给特定人员。提出了一种跌倒检测算法,根据设定的合加速度阈值和时间阈值,来检测失重、撞击、静止三个过程是否顺序发生,根据设定的姿态角阈值,检测人体姿态角是否超过正常范围,从而判断是否跌倒。测试结果表明:系统性能稳定,检测跌倒的准确率达到97%,满足人体跌倒检测的标准。
In order to find and rescue in time when the elderly fall,an intelligent system for fall detection and positioning alarm and resuce is designed. STM32 F103 ZET6 is used as the microprocessor of the system,triaxial acceleration sensor ADXL345 is used to obtain acceleration data. And ATK—NEO—6 M is used to realize global positioning. When the elderly fall,SIM900 A sends a message containing location information for help to a specific person. An algorithm for fall detection is proposed. The resultant acceleration threshold and time threshold are used to determine whether the three processes of weightlessness,impact and rest occurred sequentially. The human attitude angle is judged by the set attitude angle threshold whether it exceeds normal range,and the system determines whether or not the person fell eventually. Test results show that the accuracy of fall detection is 97 %.The system has stable performance and meets the standard of human fall detection.
In order to find and rescue in time when the elderly fall,an intelligent system for fall detection and positioning alarm and resuce is designed. STM32 F103 ZET6 is used as the microprocessor of the system,triaxial acceleration sensor ADXL345 is used to obtain acceleration data. And ATK—NEO—6 M is used to realize global positioning. When the elderly fall,SIM900 A sends a message containing location information for help to a specific person. An algorithm for fall detection is proposed. The resultant acceleration threshold and time threshold are used to determine whether the three processes of weightlessness,impact and rest occurred sequentially. The human attitude angle is judged by the set attitude angle threshold whether it exceeds normal range,and the system determines whether or not the person fell eventually. Test results show that the accuracy of fall detection is 97 %.The system has stable performance and meets the standard of human fall detection.
引文
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[2]刘非非.基于视频监控的室内跌倒行为的检测与识别研究[D].济南:山东大学,2016.
[3]王莉,赵艳阳,刘雪峰,等.基于惯性传感器的跌倒检测技术研究进展[J].科学技术与工程,2017,17(32):205-213.
[4]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.
[5]陈鹏,涂亚庆,童俊平,等.可穿戴式跌倒检测智能系统设计[J].传感器与微系统,2017,36(2):114-116.
[6]胡代弟,王小丽.基于ADXL345三轴加速度计的倾角测量系统[J].电子测试,2015,22(19):113-115.
[7]李飞龙.基于三轴加速度传感器跌倒检测方法的研究[D].成都:电子科技大学,2015.
[8]王荣,章韵,陈建新.基于三轴加速度传感器的人体跌倒检测系统设计与实现[J].计算机应用,2012,32(5):1450-1452,1456.
[9]王振,肖明波.数据融合的跌倒检测系统设计[J].传感器与微系统,2017,36(6):86-89.
[10]陈玮,周晴,曹桂涛.基于SVM和阈值分析法的摔倒检测系统[J].计算机应用与软件,2017,34(7):182-187,276.
[11]张金桥.基于人体姿态的跌倒检测算法研究与应用[D].大连:大连海事大学,2016.
[12]任志玲,张冰倩,郑丽媛.基于加速度传感器的跌倒检测与报警系统设计[J].计算机测量与控制,2013,21(6):1428-1430,1433.