联合毫米波感知与机器视觉的生猪呼吸心率同步监测方法

Pig Breathing and Heart Rates Synchronous Monitoring Method Using Millimeter-wave Perception and Machine Vision

摘要:

在现代畜牧业中，生猪的呼吸心率是评估其健康状态的关键指标之一。因此，开发一种非接触式、高精度的多目标生命体征监测技术对于推动畜牧业的现代化发展具有重要意义。毫米波雷达技术通过发射线性调频连续波（Linear frequency modulated continuous wave，LFMCW），能够实现极高的脉冲压缩比，从而显著提升雷达的距离分辨率和目标检测能力。针对现有方法在多目标呼吸心率同步监测能力上的不足，本文提出一种联合机器视觉与毫米波感知的多生猪目标呼吸心率同步监测方法。通过YOLO v8算法识别图像中的生猪目标，有效去除非生猪目标振动源，为毫米波雷达提供先验条件，进而通过LFMCW的相量均值相消算法和二维傅里叶变换方法进行静态目标去除和多目标回波解耦，提取回波信号后，通过带通滤波、短时傅里叶变换、周期性评估指标等方法提取目标呼吸心跳时频图并计算呼吸心率。为了验证该方法的有效性，在养殖场实际场景下进行多次实验，结果表明，该方法对生猪呼吸频率测量的平均相对误差为4.57%，心跳频率平均相对误差为9.26%，同步监测准确率较高且对环境中非目标振动源信号具有一定抗干扰能力。

Abstract:

In modern animal husbandry, the breathing rate and heart rate of pigs are critical indicators for assessing their health status. Therefore, the development of a non-contact, high-precision multi-target vital sign monitoring technology holds significant importance for advancing the modernization of the livestock industry. Millimeter-wave radar technology, by transmitting linear frequency modulated continuous wave (LFMCW), achieves an extremely high pulse compression ratio, thereby significantly enhancing radar range resolution and target detection capabilities. To address the limitations of existing methods in synchronous multi-target breathing rate and heart rate monitoring, a synchronous multi-pig breathing rate and heart rate monitoring method was proposed, which integrated machine vision with millimeter-wave sensing. The YOLO v8 algorithm was employed to identify pig targets in images, effectively eliminating non-pig vibration sources and providing prior conditions for millimeter-wave radar. Subsequently, the phasor mean cancellation algorithm for LFMCW and two-dimensional Fourier transform method were utilized to remove static targets and decouple multi-target echoes. After extracting echo signals, time-frequency diagrams of breathing and cardiac activities were generated through bandpass filtering, short-time Fourier transform, and periodic evaluation metrics to calculate breathing rate and heart rates. To validate the effectiveness of the proposed method, multiple experiments were conducted in practical farm environments. Results demonstrated that the average relative error for breathing rate measurement was 4.57%, and that for heart rate measurement was 9.26%, indicating high synchronization accuracy and notable anti-interference capabilities against non-target vibration sources in the environment.