近年来，人们越来越认识到健康的重要性，对于身体健康的关注不再仅仅局限于对疾病的防治，而是更加关心在日常生活中对身体健康的监测。传统的医疗健康监测设备由于存在需要医学设备辅助、非医务人员操作困难等问题，并不适用于对个人健康的实时监测。人们日益倾向采用便携式的智能穿戴设备对体征数据进行采集。随着无线通信技术、芯片技术和传感器技术的快速发展，为健康监测辅助系统提供了技术支持。在此背景下，本文设计了监测呼吸频率、心率和体表温度的基于穿戴式设备的远程健康监测系统。

首先，设计一款胸带设备，根据监测的人体相关数据判断身体状况，并进行报警。本系统选用MPU-6050惯性传感器、BMD101心电传感器和DS18B20温度传感器安装在胸带设备上，分别采集呼吸频率、心率和体表温度。采集的数据经过处理后，通过NB-IoT模块发送至服务器处理和存储，家人或医生可以通过手机APP查看相关体征数据和身体健康评估结果，实现实时健康监测。

其次，研究滤波算法原理基础上，将卡尔曼滤波算法运用到本系统的呼吸频率计算中，即对通过传感器获得的三轴加速度和三轴角速度分别运用滤波算法，缩小了三轴加速度和三轴角速度的测量误差，提高了呼吸频率的测量准确度。

最后，设计并实现了相关服务器数据管理软件和手机APP程序，实现了人机交互，方便家人和医生等相关人员通过手机APP实时查询被测人的健康状况。

本系统测试结果表明，加入卡尔曼滤波算法对测量呼吸频率的准确性有很大的提升。进行对比测试，本系统所测的呼吸频率、心率和体表温度的平均相对误差分别在3.81%、0.84%和0.28%左右，可用于日常健康监测。本系统对独居老人的健康监测、慢性病患者的日常健康管理、户外探险爱好者在探险时健康监测及军人在战场上的健康状态监测等场合有着现实意义。

In recent years, more and more people have realized the importance of health, and the attention on health is no longer limited to the prevention and treatment of diseases, but more concerned about the monitoring of health in daily life. Traditional medical health monitoring equipment is not suitable for real-time monitoring of personal health because of the need for medical equipment and the difficulty of operation for non-medical personnel. People are increasingly inclined to adopt portable intelligent wearable devices to collect physical signs data. With the rapid development of wireless communication technology, chip technology and sensor technology, it provides technical support for health monitoring auxiliary system. In this context, this paper designed a wearable device-based remote health monitoring system to monitor respiratory rate, heart rate and body surface temperature.

First, a chest strap device is designed to judge the physical condition based on the monitored human-related data and alarm. This system selects MPU-6050 inertial sensor, BMD101 ECG sensor and DS18B20 temperature sensor to be installed on the chest strap device to collect respiratory rate, heart rate and body surface temperature respectively. After the collected data is processed, it is sent to the server for processing and storage through the NB-IoT module. Family members or doctors can view the relevant physical signs data and physical health assessment results through the mobile APP to realize real-time health monitoring.

Secondly, on the basis of studying the principle of the filtering algorithm， the Kalman filtering algorithm is applied to the respiratory rate calculation of this system, that is, the filtering algorithm is applied to the three-axis acceleration and three-axis angular velocity obtained by the sensor, and the three-axis acceleration and three-axis angular velocity are reduced. The measurement error of angular velocity improves the measurement accuracy of respiratory rate.

Finally, the remote server was built, and the relevant server data management software and mobile APP were designed and implemented to realize human-computer interaction and facilitate family members, doctors and other relevant personnel to query the health status of the tested person in real time through the mobile APP.

The test results of this system show that adding Kalman filter algorithm can greatly improve the accuracy of respiration rate measurement. The difference between the respiratory rate, heart rate and body surface temperature measured by this system and the comparison test is about 3.81%, 0.84% and 0.28%, respectively, which can be used for daily health monitoring. The system has practical significance for the health monitoring of the elderly living alone, the daily health management of patients with chronic diseases, the health monitoring of outdoor adventurers during exploration and the health monitoring of soldiers on the battlefield and other occasions.

1赵警. 基于多传感器信息融合的健康辅助监测技术[D].北京化工大学，2020.
2曾甜. 可穿戴血氧传感器监测系统的研究及其在物联网健康中的应用[D].浙江大学，2018.
3Nascimento L, Bonfati L V , Freitas M , et al. Sensors and Systems for Physical Rehabilitation and Health Monitoring—A Review[J]. Sensors, 2020, 20(15):4063.
4Floris C,Solbiati S, Landreani F, et al. Feasibility of Heart Rate and Respiratory Rate Estimation by Inertial Sensors Embedded in a Virtual Reality Headset[J]. Sensors, 2020，20(24):7168.
5Amft O ,Lukowicz P . From Backpacks to Smartphones: Past,Present,and Future of Wearable Computers[J]. IEEE Pervasive Computing,2009, 8(3):8-13.
6米兰综合工科大学. 用于连续监测呼吸率的可穿戴设备:CN201880052723.2[P]. 2020-04-17.
7王宪忠. 穿戴式多生理参数健康监测系统的研制[D].吉林大学，2017.
8Ru,Lei等.A Detailed Research on Human Health Monitoring System Based on Internet of Things[J].WIRELESS COMMUNICATIONS AND MOBILE COMPUTING，2021，2021.7 doi:10.1155/2021/5592454.
9Zahid Muhammad,Anwer Rathore Hassaan,Tayyab Hamna,Ahmad Rehan Zulfiqar,Abdul Rashid Iqra,Lodhi Maria,Zubair Usman,Shahid Imran. Recent developments in textile based polymeric smart sensor for human health monitoring: A review[J]. Arabian Journal of Chemistry,2022,15(1).doi:10.1016/J.ARABJC.2021.103480.
10智能心电衣Smart Vest[J]. 消费电子，2017，0(8):13.
11陆喆俊，孙宪坤，陶鹏.基于加速度计模型重构的自适应姿态融合算法[J].传感器与微系统，2021，40(08):133-136.
12徐鑫，赵鹤鸣.基于改进型卡尔曼滤波的运动载体姿态估计[J].传感技术学报，2020，33(09):1279-1284.
13杨环宇. 基于角速度信号的呼吸参数提取研究及应用[D].深圳大学，2017.
14广东中科慈航信息科技有限公司. 一种基于陀螺仪测量呼吸的方法及设备:CN201810122013.4[P]. 2018-10-02.
15丁青锋，王丽姚.基于复合传感器的人体健康监测辅助系统设计[J].传感器与微系统，2019，38(11):82-84+88.
16高鹏彪. 人体生物信息的检测与健康监测系统的应用[D].吉林大学，2019.
17杨海，罗涛，李莉，梁海波，张禾.基于惯性测量单元的运动员呼吸状态监测方法[J].中国惯性技术学报，2018，26(05):591-596.
18孟瑶.基于可穿戴传感器的实时心电检测方法研究[J].信息技术与网络安全，2021，40(04):75-79.
19Kalgotra P, Sharda R, Hammer B , et al. Sensor Analytics for Interpretation of EKG Signals[J]. Expert Systems with Applications, 2019, 123(JUN.):377-385.
20郭晓曦. NB-IoT终端安全协议一致性测试集的设计与实现[D].北京邮电大学，2018.
21宋洪儒，王宜怀，杨凡.基于窄带物联网智能燃气表系统设计与实现[J].传感器与微系统，2019，38(03):113-116.
22余勇昌，张典，丁明玲.5G时代4G/5G共天馈面解决方案[J].电信科学，2021，37(04):140-150.
23Hossain M.,Haque M.E.,Arif M.T.. Kalman filtering techniques for the online model parameters and state of charge estimation of the Li-ion batteries: A comparative analysis[J]. Journal of Energy Storage,2022,51.doi:10.1016/J.EST.2022.104174.
24何莉鹏，张一茗，张文涛，李少华.基于卡尔曼滤波的高压断路器机械特性监测系统设计[J].自动化与仪表，2022，37(09):47-52.DOI:10.19557/j.cnki.1001-9944.2022.09.011.
25Arpaia Pasquale,Buzio Marco,Di Capua Vincenzo,Grassini Sabrina,Parvis Marco,Pentella Mariano. Drift-Free Integration in Inductive Magnetic Field Measurements Achieved by Kalman Filtering[J]. Sensors,2021,22(1).doi:10.3390/S22010182.
26邓思源，周兰庭，柳志坤.基于卡尔曼滤波、分形和LSTM的大坝变形趋势分析方法[J].水利水电科技进展，2022，42(05):121-126.
27刘亮，陈仙亮.卡尔曼滤波能耗预测模型及其应用[J/OL].电力系统及其自动化学报:1-8[2022-10-13].DOI:10.19635/j.cnki.csu-epsa.001040.
28龙子洋，项鹏，隋国荣.基于扩展卡尔曼滤波的球形机器人姿态解算[J].软件工程，2022，25(07):47-50.DOI:10.19644/j.cnki.issn2096-1472.2022.007.011.
29廖坤男. 四旋翼无人机姿态解算与控制方法研究[D].西南科技大学，2022.DOI:10.27415/d.cnki.gxngc.2022.000468.
30Sensor Research; Data from S. Martiradonna et al Provide New Insights into Sensor Research (On the Evaluation of the NB-IoT Random Access Procedure in Monitoring Infrastructures)[J]. Journal of Technology,2019.
31Sakshi Popli,Rakesh Kumar Jha,Sanjeev Jain. A Survey on Energy Efficient Narrowband Internet of Things (NBIoT): Architecture,Application and Challenges.[J]. IEEE Access,2019,7.
32Lam D. Nguyen,Anders E. Kalor,Israel Leyva Mayorga,Petar Popovski. Trusted Wireless Monitoring Based on Distributed Ledgers over NB-IoT Connectivity[J]. IEEE Communications Magazine,2020,58(6). doi:10.1109/mcom.001.2000116.
33冯娜娜，张晴，杨延宁.基于树莓派的多功能体温计设计[J].自动化与仪器仪表，2021(08):211-214.DOI:10.14016/j.cnki.1001-9227.2021.08.211.
34李想.温度传感器DS18B20在温室大棚中的实现[J].南方农机，2020，51(16):182-183.
35张旭伟，苏涵彬.DS18B20温度传感器在非实时操作系统下的应用[J].单片机与嵌入式系统应用，2021，21(05):84-86.
36全斐.基于MSP430g2553的多路温度巡检系统[J].电子设计工程，2020，28(10):51-55.DOI:10.14022/j.issn1674-6236.2020.10.011.
37Zhendong Ai,Zihan Wang,Wei Cui. Low-Power Wireless Wearable ECG Monitoring Chestbelt Based on Ferroelectric Microprocessor[C]//.第三十八届中国控制会议论文集（5）.,2019:148-153.
38骆方正，蒋明峰，张鹏，张鞠成，王志康.基于BMD101可穿戴式心电监测系统设计[J].电子测量技术，2021，44(21):113-119.DOI:10.19651/j.cnki.emt.2107313.
39Cimolin Veronica,Gobbi Michele,Buratto Camillo,Ferraro Samuele,Fumagalli Andrea,Galli Manuela,Capodaglio Paolo. A Comparative Analysis of Shoes Designed for Subjects with Obesity Using a Single Inertial Sensor: Preliminary Results[J]. Sensors,2022,22(3).doi:10.3390/S22030782.
40Lee Hyeonil,Shahzad Ahsan,Kim Kiseon. Automated prescreening of MCI through deep learning models based on wearable inertial sensors data[J]. Alzheimer's & Dementia,2021,17.doi:10.1002/ALZ.052744.
41党娇娇，于艺，逯波，张美玉，宋军，闪增郁.平人红外热图年节律变化规律研究[J].世界中医药，2021，16(18):2775-2778+2783.