基于压电电缆传感器的心率测量与反馈系统设计
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摘要
心率作为人体最基本的生理参数之一,反映着人体健康变化状况。为提高心率监测的实时性与准确性,设计了一种基于压电电缆传感器的非接触式心率监测系统。该系统利用心冲击图又称体震(BCG)信号记录心脏活动引起的身体震动,并通过FIR数字滤波器进行信号去噪,然后利用差分阀值检测算法进行波形特征提取。为实现对失眠症状的自适应调节,设计了基于磁诱导模块的反馈装置产生24 Gs～72 Gs的磁场作用于神经中枢。实验测试结果表明,与医用仪器相比,心率测量误差约为±3次/分,而磁诱导模块改善了非自主神经的兴奋与抑制水平间接提高了睡眠质量,符合未来家庭监测仪器的发展方向。
As one of the most basic physiological parameters of the human body,heart rate reflects the changes in human health. In order to improve the real-time and accuracy of heart rate detection,a non-contact heart rate monitoring system based on piezoelectric cable sensor is designed. The system records body vibration caused by the heart activity through ballistocardiogram (BCG) signal. The BCG signal is denoised by using FIR digital filter,and then the waveform features are extracted by utilizing the differential threshold detection algorithm. In order to achieve selfadaptive adjustment of insomnia symptoms,a feedback device based on the magnetic induction module is designed to generate a magnetic field of 24 Gs to 72 Gs acting on the nerve center. The experimental test results show that the heart rate measurement error is about ± 3 beats/min compared with medical instruments,and the magnetic induction module improves the excitement and inhibition level of non-autonomic nerves,which indirectly improves the quality of sleep. This is in line with the future development direction of home monitoring equipment.
As one of the most basic physiological parameters of the human body,heart rate reflects the changes in human health. In order to improve the real-time and accuracy of heart rate detection,a non-contact heart rate monitoring system based on piezoelectric cable sensor is designed. The system records body vibration caused by the heart activity through ballistocardiogram (BCG) signal. The BCG signal is denoised by using FIR digital filter,and then the waveform features are extracted by utilizing the differential threshold detection algorithm. In order to achieve selfadaptive adjustment of insomnia symptoms,a feedback device based on the magnetic induction module is designed to generate a magnetic field of 24 Gs to 72 Gs acting on the nerve center. The experimental test results show that the heart rate measurement error is about ± 3 beats/min compared with medical instruments,and the magnetic induction module improves the excitement and inhibition level of non-autonomic nerves,which indirectly improves the quality of sleep. This is in line with the future development direction of home monitoring equipment.
引文
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[2]刘一,任占兵.基于安卓手机的远程心电测量系统的设计[J].电子器件,2015,38(1):194-197.
[3]刘祎,欧阳健飞.基于人脸视频的非接触式心率测量方法[J].纳米技术与精密工程,2016,14(1):76-79.
[4]王春武,王旭,龙哲,等.坐立两便式心冲击信号检测系统设计与实现[J].东北大学学报(自然科学版),2012,33(6):786-789,800.
[5]蒋杏国.基于压电电缆的车辆动态称重系统设计[D].南京:南京理工大学,2011.
[6]覃才珑,林义忠,黄文玲.基于红外传感器与压电电缆AGV综合防护系统研究[J].传感器与微系统,2012,31(6):15-17,21.
[7]王术新,李以哲.PVDF传感器的设计及在振动测量中的应用[J].压电与声光,2004,26(5):374-376.
[8]王庆锋,吴斌,宋吟蔚,等.PVDF压电传感器信号调理电路的设计[J].仪器仪表学报,2006,27(6):1653-1655.
[9]蒋学慧.经颅磁刺激理论研究及磁诱导睡眠仪刺激线圈设计[D].天津:天津大学,2008.
[10]史晓伦.动物交变磁诱导系统的实验设计[J].实验室科学,2011,14(2):97-99.
[11]周平,汪丰,马腾飞,等.电容式非接触心电传感器的设计[J].仪表技术与传感器,2013(8):5-7.
[12]郑慧君,俞梦孙,曹征涛,等.基于微动敏感床垫式睡眠监测系统的心率检测[J].北京生物医学工程,2009,28(5):486-489.
[13]Jin J,Wang X,Li S,et al.A Novel Heart Rate Detection Algorithm in Ballistocardiogram Based on Wavelet Transform[C]//IEEEProceedings of 2009 Second International Workshop on Knowledge Discovery and Data Mining,23-25 Jan,2009,76-79.
[14]Gilaberte S,Clapers J,Casanella R,et al.Heart and Respiratory Rate Detection on a Bathroom Scale Based on the Ballistocardiogram and the Continuous Wavelet Transform[J].2010 Annual International Conference of the IEEE Engineering in Medicine and Biology Society(EMBC),31 Aug.-4 Sept,2010,2557-2560.
[15]蒋芳芳,王旭,杨丹.基于诊断特征提取的体震信号盲分帧算法研究[J].系统仿真学报,2011,23(10):2056-2059.
[16]李天涵,徐效文,梁莹.基于电容耦合的多导联心电监测系统研究[J].传感技术学报,2017,30(11):1752-1757.
[17]Zhang J H,Wu Y S,Liu Q Q,et al.Research on High-Precision,Low Cost Piezoresitive MEMS-Array Pressure Transmitters Based on Genetic Wavelet Neural Networks for Meteorological Measurements[J].Micromachines,2015,6:554-573.
[18]朱莹莹.微动敏感床垫信号中体动信息的分类识别[D].山东:山东大学,2009.
[19]冯晓明.基于腕动信号的睡眠质量监测装置设计[D].广州:华南理工大学,2014.
[20]苗旭.便携式睡眠监测系统的研究[D].哈尔滨:哈尔滨工业大学,2013.
[21]李治.磁诱导睡眠仪的效果研究及改进[D].天津:天津大学,2007.