基于Wi-Fi无线感知技术的猪呼吸频率监测

监测和及时发现猪呼吸异常是养猪产业管理中的重要课题。为了克服人工监测方式效率低下、穿戴式设备监测方法成本较高且容易引起猪应激反应的缺点,该文提出了一种基于Wi-Fi网络信道状态信息的非接触式猪呼吸率监测方案。首先,利用Wi-Fi网络设备及其开源驱动程序捕获CSI序列信号并提出异常载波过滤算法用于滤除通信过程中的异常载波;其次,设计载波周期性水平量化指标并以此评估载波周期性水平;第三,通过SmoothingSplines方法平滑载波曲线并基于载波序列自相关函数估计载波周期和频率,筛选出载波周期性水平大于22且频率位于闭区间0.127 Hz,1.25 Hz]的反映猪只呼吸行为的载波;第四,对符合条件的载波频率进行加权平均求得猪只呼吸率。以人工统计猪只每分钟的呼吸次数作为真实情况,通过对9头仔猪,5头育肥种猪,3头怀孕母猪以及3头因患病引起腹式呼吸的病猪进行对比试验,该文提出的方法能够准确计算出猪的呼吸率,平均相对误差为1.398%。研究结果为应用Wi-Fi无线感知技术监控动物呼吸率提供参考。

Monitoring pig respiration frequency using Wi-Fi wireless sensing technology

Abstract: Monitoring pig respiration timely in swine farms is critical to safeguard swine production. Traditional manual method by tagging pigs with sensors is inefficient and makes pigs stressful. Figuring out non-contact and non-destructive ways is hence necessary. Wi-Fi technology is non-intrusive and robust, and it has received increasing attention over the past few years as a potential method to track animal respiration. Its fundamental principle is that the exhaling - inhaling cycle in pig respiration results in a small change in the Wi-Fi signals when they propagate from transmitter to receiver. In the 802.11 a/g/n standard, the signal response in the channel can be partially extracted from the off-the-shelf OFDM receivers in the format of Channel State Information, which revealed that a set of channel measurements can indeed pick up such change, making it feasible to monitor animal respiration. We proposed a novel method based on the Wi-Fi signal in this paper to estimate the respiration rate of pigs reared in a single shed. We obtained the motion-state data in the CSI data files first using the off-the-shelf Wi-Fi devices commonly used in daily life. The CSI data is matrix of 1×3×30, where 1 is the number of transmit antennas, 3 is the number of receive antennas and 30 is the number of subcarriers in one beam. Preprocessing these data and evaluating the carrier periodicity level enabled us to identify the CSI signal sequences that contain the abdomen undulation of pigs. This is followed by smoothing the subcarrier curve with the algorithm of smoothing spline and evaluating the period and frequency of the subcarrier with the self-correlation function of CSI sequences. Finally, we statistically estimated the weighting average of the multiple subcarrier frequencies to calculate the respiration rate of pig. Taking the number of breaths manually accounted per minute from the pigs as ground truth, the proposed method was tested against the respiration data measured from 9 piglets, 5 fattening pigs, 3 pregnant sows and 3 sick pigs with abdominal breathing caused by illness. The results show that the maximum relative error is 3.18%, the minimum relative error is -3.45%, and the average relative error is 1.4%. The study has wide implications in using Wi-Fi technology to monitor respiration of animal.