Lameness Detection in Cows Using Hierarchical Deep Learning and Synchrosqueezed Wavelet Transform
Lameness Detection in Cows Using Hierarchical Deep Learning and Synchrosqueezed Wavelet Transform
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DOI：
10.1109/jsen.2021.3054718
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发表时间：
2021-04-01
影响因子：
4.3
通讯作者：
Sanei, Saeid
中科院分区：
文献类型：
--
作者：
Jarchi, Delaram;Kaler, Jasmeet;Sanei, Saeid
目的：奶牛跛行的识别对农民改善和管理牛的健康和福利具有重要意义。没有经过验证的自动跛行检测工具。在这项研究中，我们的目标是早期检测奶牛跛行，通过识别瞬时的基本步态谐波从低频（16 Hz）的加速度信号记录使用腿戴传感器。方法：三轴加速度计已被佩戴在每个牛腿。同步压缩小波变换（SSWT）已被应用到加速度信号产生的初始时频谱的步态。该频谱作为设计的深度神经网络的输入，包括基于时间-频率的长短期记忆（LSTM），以估计每个时间点的瞬时频率。然后使用逆SSWT（ISSWT）来恢复步态谐波并估计增强的频谱。结果如下：已为每个牛腿（来自23头牛的组合信号）提供了瞬时频率的验证，并提供了三个折叠的时间序列交叉验证器。对于左前腿、右前腿、右后腿和左后腿，获得了每条腿的3次折叠频率的均方误差的平均值分别为0.036、0.033、0.044和0.042。结论：瞬时步态频率的估计被证明是有用的识别奶牛步态相位，跛行检测，准确估计步态速度，腿之间的运动的连贯性和识别非步态发作。此外，该方法可以作为一种新的频率脊估计方法，利用SSWT的许多其他应用。
Objectives: Identification of cow lameness is important to farmers to improve and manage cattle health and welfare. No validated tools exist for automatic lameness detection. In this research, we aim to early detect the cow lameness by identifying the instantaneous fundamental gait harmonics from low frequency (16Hz) acceleration signals recorded using leg-worn sensors. Methods: A triaxial accelerometer has been worn on each cow leg. Synchrosqueezed wavelet transform (SSWT) has been applied to acceleration signals to generate the initial time-frequency spectrum related to the gait. This spectrum is given as an input to a designed deep neural network including time-frequency based long short-term memory (LSTM) to estimate instantaneous frequencies at each time point. An inverse SSWT (ISSWT) is then used to recover the gait harmonic and to estimate an enhanced spectrum. Results: Validation of instantaneous frequencies has been provided for each cow leg (combined signals from 23 cows) and the time-series cross validator across the three folds are provided. The average of mean squared errors in frequencies across 3 folds for each leg is obtained as 0.036, 0.033, 0.044 and 0.042 for left-front, right-front, right-back and left-back legs, respectively. Conclusion: Estimation of instantaneous gait frequencies is proved useful for identification of cow gait phases, lameness detection, accurate estimation of gait speed, coherency in movement among the legs and identification of non-gait episodes. Moreover, the proposed method can be used as a new frequency ridge estimation method exploiting SSWT for many other applications.