基于RNN的脉搏波血压计的研究与实现

摘要

针对于传统袖带血压计的不便捷性和不连续性,设计了一种便携式的脉搏波血压计.该血压计通过光电传感器采集指端的脉搏波信号;在微控制器中对脉搏波信号进行预处理及血压的计算,其中通过训练优化好的循环神经网络预测模型来预测计算血压值;将预测出来的血压值显示到OLED显示屏,最终实现实时连续血压的监测.经测试,该便携式脉搏波血压计的血压预测误差在±5 mmHg之内,符合国际血压计测量的误差范围.所设计的便携式基于RNN的脉搏波血压计为智能穿戴健康监护设备的开发和设计提供了较高的参考价值.%Aiming at the inconvenience and discontinuity of traditional cuff sphygmomanometer,a portable pulse wave sphygmomanometer is designed.The sphygmomanometer collects the pulse wave signal of the finger end through the photoelectric sensor,preprocesses the pulse wave signal and the blood pressure in a micro controller,and predicts and calculates the blood pressure value by training and optimizing a circulating neural network prediction model.Blood pressure values will be displayed on the OLED display,ultimately realizing real-time continuous blood pressure monitoring.After tested,portable pulse wave sphygmomanometer blood pressure prediction error is within plus or minus 5 mmHg,in line with international sphygmomanometer measurement error range.The portable RNN-based pulse wave sphygmomanometer designed in this paper provides a high reference value for the development and design of intelligent wear health monitoring equipment.