基于STM32和小波自适应滤波算法的生理参数监测系统的研究
2023,31(2):76-82
摘要：针对心血管疾病的高死亡率以及人口老龄化的现象，本篇文章开发了基于STM32单片机和小波自适应阈值滤波算法的可穿戴式健康监测系统。系统可分为系统微处理器、数字系统模块、人机交互模块、信号采集模块和无线通信模块等几个部分，针对人体的心率、血氧、体温等重要生理参数进行处理分析，进而对人体实时监护。系统处理器选取STM32F103C8T6作为控制芯片，显示模块选用了OLED。生理参数采集系统选用了MAX30102传感器和Pulse sense传感器分别对人体腕部和指尖心率进行采集。生理参数采集完毕后，通过进一步的A/D转化，基于提出的一种改进小波自适应阈值滤波算法降噪滤波，从而将人体的生理特征参数记录下来。再将采集的生理数据通过蓝牙传输至手机端，其中的ZigBee模块主要是把获得的数据再次输送到远程控制端内,让患者能够远程得到更好的医疗监控。本系统通过软件与硬件相结合的方式。最后通过对比论证其中心率（BPM）结果误差为±2BPM，血氧含量监测结果误差在±2%以内。
关键词：医疗监护；小波变化；自适应滤波；生理参数多点监测；数据采集
Study on physiological parameter monitoring system based on STM32 and wavelet adaptive filtering algorithm
Abstract：In view of the high mortality rate of cardiovascular disease and the phenomenon of population aging, this article develops a wearable health monitoring system based on STM32 microcontroller and wavelet adaptive threshold filtering algorithm. The system can be divided into several parts, such as system microprocessor, digital system module, human-computer interaction module, signal acquisition module and wireless communication module, which are processed and analyzed for important physiological parameters such as human heart rate, blood oxygen, and body temperature, and then monitor the human body in real time. The system processor selects STM32F103C8T6 as the control chip, and the display module uses OLED. The physiological parameter acquisition system uses the MAX30102 sensor and the Pulse sense sensor to collect the heart rate of the human wrist and fingertips, respectively. After the physiological parameters are collected, the physiological characteristics of the human body are recorded by further A/D conversion, based on the proposed improved wavelet adaptive threshold filtering algorithm to reduce noise filtering. The collected data is then transmitted to the mobile phone through Bluetooth, and the ZigBee module is mainly to transmit the obtained data to the remote control terminal again, so that patients can get better medical monitoring remotely. The system is a combination of software and hardware. Finally, the error of the center rate (BPM) result is 2 BPM, and the error of the blood oxygen content monitoring result is within 2%.
Key words：medical monitoring; Wavelet changes; Adaptive filtering; Multi-point monitoring of physiological parameters; Data acquisition
收稿日期：2022-06-30
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