引入时距信息的IR-UWB雷达多域特征融合呼吸模式识别方法

IR-UWB Radar Multi-domain Feature Fusion Respiratory Pattern Recognition Method with Time-distance Information

人体呼吸系统相关疾病常常伴随着呼吸深度和节律的异常,因此呼吸信号监测和呼吸模式识别在医疗健康领域中尤其是对于睡眠监测、疾病预断具有重要意义。其中,非接触式的脉冲式超宽带雷达(Impulse Radio Ultra-Wideband,IR-UWB)因具有良好的距离分辨率和穿透能力以及全天候全天时、安全无创的检测优势,正逐步成为睡眠健康监护领域中最关键的感知技术之一。然而受睡眠监测特定的室内场景影响,复杂的测量环境给呼吸模式特征的准确提取带来了限制和挑战,传统的雷达呼吸模式识别算法主要关注一维呼吸时、频域特征,而IR-UWB雷达目标回波信息分散在多个距离门内,使用一维特征识别准确率较低。为此,本文针对IR-UWB雷达中人体呼吸在时间上慢速起伏运动、在距离上是扩展目标的信号模型特点,提出了一种引入时距信息的IR-UWB雷达多域特征融合呼吸模式识别方法。算法在提取一维呼吸信号波形时、频域特征的基础上更进一步挖掘雷达二维时距图像中潜在的呼吸模式形态特征,通过多域特征融合实现呼吸模式的非接触式检测和识别。在图像处理上,针对图像受呼吸异常节律影响呈现局部粘连特性导致呼吸周期提取难的问题,提出一种通过相位矩阵图像处理来检测雷达图像中的呼吸时距条带从而获取图像特征的方法。实验结果表明,利用该算法提取的多域特征对六种呼吸模式进行机器学习的分类识别,可以实现96.3%的识别准确率。

Diseases related to the human respiratory system are often accompanied by abnormalities in the respiratory depth and rhythm.Respiratory signal monitoring and respiratory pattern recognition are very important in the medical and health fields,especially for applications such as sleep monitoring and disease prediction.Among the various methods utilized,non-contact impulse radio ultra-wideband(IR-UWB)radar is gradually becoming one of the most critical sensing technologies in the field of sleep health monitoring because of its excellent range resolution and penetration ability,as well as the advantages of all-weather,all-time,safe,and non-invasive detection.However,a complex indoor measurement environment poses limitations and challenges to the accurate extraction of respiratory pattern features.Traditional radar respiratory pattern recognition algorithms mainly focus on one-dimensional respiratory time and frequency domain features,whereas the echo information targeted by IR-UWB radar is scattered within multiple range bins,which causes a low accuracy when using one-dimensional features for recognition.Therefore,this paper proposes a multi-domain feature fusion respiratory pattern recognition method with time-distance information for IR-UWB radar,which focuses on the characteristics of a signal model in which human respiration is an extended target in distance and slowly fluctuates in time.Based on extracting the time and frequency domain features of one-dimensional respiratory signals,the algorithm was used to further explore the potential respiratory pattern morphological features in two-dimensional respiratory radar images.The non-contact detection and recognition of respiratory patterns were achieved based on the multi-domain feature information of respiratory signals in time and distance.An image-processing method was proposed to detect respiratory time-distance bands in radar images,Phase-matrix image processing was used to obtain image features and address the problem of local adhesion in images affected by abnormal respiratory rhythms,which make it difficult to extract respiratory cycles.Finally,in an experiment involving the machine learning classification and recognition of six respiratory patterns,the multi-domain features extracted by this algorithm achieved a recognition accuracy of 96.3%.The experimental results showed that this method could capture richer spatiotemporal information about respiratory patterns,providing a new approach for respiratory pattern recognition.It demonstrated better respiratory-pattern recognition performances than traditional methods,indicating that the proposed approach has potential for sleep-state monitoring and assisted disease diagnosis.