老年人健康监测的柔性传感器测量信号校准  

Calibration of Flexible Sensor Measurement Signals for Elderly Health Monitoring

作　　者：汪洋[1] WANG Yang(Intelligent Manufacturing School,Huainan Union University,Huainan 232000,Anhui,China)

机构地区：[1]淮南联合大学智能制造学院,安徽淮南232000

出　　处：《西昌学院学报（自然科学版）》2024年第3期60-68,共9页Journal of Xichang University(Natural Science Edition)

基　　金：安徽省教育厅2022年高校优秀青年人才支持项目(gxyq2022200)。

摘　　要：[目的]对老年人健康状态变化的准确监测过程中,基础的误差反向传播(back propagation,BP)神经网络难以对柔性传感器测量信号进行精准的校准处理,导致容易出现过拟合现象,使得校准后的信号均方根差(root mean square error,RMSE)较大。因此,以面向老年人健康监测的柔性传感器为研究对象,设计一种基于改进GA-BP神经网络的测量信号校准方法。[方法]将卡尔曼滤波算法和滑动平均滤波算法结合起来,对柔性传感器实时测量信号进行混合滤波处理,得到去除噪声干扰的有效信号。通过细分操作将预处理后的信号转换为多个信号子序列,并计算出信号均方根值和波动系数,完成信号特征向量提取。以BP神经网络为核心,构建柔性传感器测量信号校准模型,并应用改进遗传算法(genetic algorithm,GA)对模型参数进行寻优计算,提升网络模型工作性能,将特征向量输入其中自动预测未来时刻健康监测信号变化,对比实时测量信号即可完成校准操作。[结果]实验结果表明:应用该方法对柔性传感器给出的老年人健康监测信号校准后,测量信号的RMSE值低于0.07。[结论]所提出的改进GA-BP神经网络的测量信号校准方法,满足了信号误差校准要求。[Objective]In the process of accurate monitoring of health status changes of the elderly,the basic error back propagation(BP)neural network is difficult to accurately calibrate the measurement signals of flexible sensors,resulting in easy overfitting phenomenon,which makes the calibrated signals have a large root mean square error(RMSE).There‐fore,a calibration method based on improved genetic GA-BP neural network is designed for flexible sensors for elderly health monitoring.[Method]The Kalman filtering algorithm and sliding average filtering algorithm were combined to per‐form hybrid filtering processing on the real-time measurement signals of the flexible sensors to obtain the effective signals with noise interference removed.The pre-processed signal was converted into multiple signal sub-sequences by segmen‐tation operation,and the signal root mean square value and fluctuation coefficient were calculated to complete the signal feature vector extraction.With BP neural network as the core,the calibration model of flexible sensor measurement signal was constructed,and the improved genetic algorithm(GA)was applied to optimize the model parameters to improve the performance of the network model,and the feature vectors were input to automatically predict the changes of the health monitoring signals in the future moments,and the calibration can be completed by comparing with real-time measurement signals.[Result]The experimental results show that after applying the method to calibrate the health monitoring signals of the elderly given by the flexible sensors,the RMSE values of the measured signals are lower than 0.07.[Conclusion]The proposed improved GA-BP neural network calibration method for measurement signals satisfies the signal error calibration requirements.

关 键 词：柔性传感器 健康监测 混合滤波 BP神经网络 遗传算法 信号校准 

分 类 号：TP212[自动化与计算机技术—检测技术与自动化装置]