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.

中文翻译：

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基于分层深度学习和同步小波变换的母牛行检测

目标：确定牛的me行状况对农民改善和管理牛的健康和福利非常重要。不存在用于自动la行检测的经过验证的工具。在这项研究中，我们的目标是通过从使用腿戴式传感器记录的低频（16Hz）加速度信号中识别瞬时基本步态谐波来尽早发现母牛的me行。方法：每只牛腿上都佩戴了三轴加速度计。同步小波变换（SSWT）已应用于加速度信号，以生成与步态有关的初始时间频谱。将此频谱作为已设计的深度神经网络的输入，其中包括基于时间-频率的长期短期记忆（LSTM），以估计每个时间点的瞬时频率。然后使用反SSWT（ISSWT）恢复步态谐波并估计增强频谱。结果：已经为每条牛腿提供了瞬时频率的验证（来自23头牛的组合信号），并且提供了三折的时间序列交叉验证器。对于左腿，右腿，右后腿和左后腿，每条腿的3倍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对确定母牛的步态阶段，la行检测，准确估计步态速度，双腿间运动的连贯性和非步态发作的识别是有用的。而且，