Accurate objective, automated limb lameness detection and pose estimation play an important role for animal well-being and precision livestock farming. We present a wearable sensor-based limb lameness detection and pose estimation for horse walk and trot locomotion. The gait event and lameness detection are first built on a recurrent neural network (RNN) with long short-term memory (LSTM) cells. Its outcomes are used in the limb pose estimation. A learned low-dimensional motion manifold is parameterized by a phase variable with a Gaussian process dynamic model. We compare the RNN-LSTM-based lameness detection method with a feature-based multi-layer classifier (MLC) and a multi-class classifier (MCC) that are built on support vector machine/K-nearest-neighbors and deep convolutional neural network methods, respectively. Experimental results show that using only accelerometer measurements, the RNN-LSTM-based approach achieves 95% lameness detection accuracy and also outperforms the feature-based MLC or MCC in terms of several assessment criteria. The pose estimation scheme can predict the 24 limb joint angles in the sagittal plane with average errors less than 5 and 10 degs under normal and induced lameness conditions, respectively. The presented work demonstrate the successful use of machine learning techniques for high performance lameness detection and pose estimation in equine science. Note to Practitioners—Automation technologies are increasingly used for precision agriculture but few have focused on monitoring individual animals in open field for precision livestock farming. Limb lameness detection and pose estimation in open field is labor-intensive, unsafe for farmers, and inefficient. The presented machine learning-enabled, wearable inertial sensor-based design provides an effective and efficient approach for horse limb lameness detection and pose estimation applications. We present an RNN-LSTM for lameness detection and an integrated manifold learning model is used to predict the horse limb joint angles in walk and trot gaits under normal and induced lameness conditions. We also present a systematic analysis and experiments to demonstrate the impacts of the wearable sensor locations and signal information on lameness detection and pose estimation performance. Several other machine learning-based lameness detection methods are also presented and compared. The extensive multi-horse testing results are presented to demonstrate the superior accuracy and higher performance than other types of machine learning methods. One attractive feature of the proposed design lies in its high performance and fast computational capability for potential real-time applications in open field.

中文翻译：

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基于可穿戴惯性传感器的马肢体跛行检测和姿势估计

准确客观、自动化的肢体跛行检测和姿势估计对于动物福利和精准畜牧业发挥着重要作用。我们提出了一种基于可穿戴传感器的肢体跛行检测和姿势估计，用于马步行和小跑运动。步态事件和跛行检测首先建立在具有长短期记忆 (LSTM) 细胞的循环神经网络 (RNN) 上。其结果用于肢体姿势估计。学习的低维运动流形由具有高斯过程动态模型的相位变量参数化。我们将基于 RNN-LSTM 的跛行检测方法与基于支持向量机/K-最近邻和深度卷积神经网络的基于特征的多层分类器 (MLC) 和多类分类器 (MCC) 进行比较方法，分别。实验结果表明，仅使用加速度计测量，基于 RNN-LSTM 的方法实现了 95% 的跛行检测准确率，并且在几个评估标准方面也优于基于特征的 MLC 或 MCC。姿势估计方案可以预测矢状面上的 24 个肢体关节角度，在正常和诱导跛行条件下，平均误差分别小于 5 度和 10 度。所提出的工作证明了机器学习技术在马科学中的高性能跛行检测和姿势估计的成功使用。姿势估计方案可以预测矢状面上的 24 个肢体关节角度，在正常和诱导跛行条件下，平均误差分别小于 5 度和 10 度。所提出的工作证明了机器学习技术在马科学中的高性能跛行检测和姿势估计的成功使用。姿势估计方案可以预测矢状面上的 24 个肢体关节角度，在正常和诱导跛行条件下，平均误差分别小于 5 度和 10 度。所提出的工作证明了机器学习技术在马科学中的高性能跛行检测和姿势估计的成功使用。从业者须知——自动化技术越来越多地用于精准农业，但很少有人专注于在开阔地监测个体动物以进行精准畜牧业。露地中的肢体跛行检测和姿势估计是劳动密集型的，对农民不安全且效率低下。所提出的基于机器学习的可穿戴惯性传感器设计为马肢跛行检测和姿势估计应用提供了一种有效且高效的方法。我们提出了一种用于跛行检测的 RNN-LSTM，并使用一个集成的流形学习模型来预测在正常和诱导跛行条件下步行和小跑步态中的马肢关节角度。我们还进行了系统分析和实验，以证明可穿戴传感器位置和信号信息对跛行检测和姿势估计性能的影响。还介绍并比较了其他几种基于机器学习的跛行检测方法。展示了广泛的多马测试结果，以证明比其他类型的机器学习方法具有更高的准确性和更高的性能。所提出的设计的一个吸引人的特点在于其高性能和快速计算能力，适用于开放领域的潜在实时应用。展示了广泛的多马测试结果，以证明比其他类型的机器学习方法具有更高的准确性和更高的性能。所提出的设计的一个吸引人的特点在于其高性能和快速计算能力，适用于开放领域的潜在实时应用。展示了广泛的多马测试结果，以证明比其他类型的机器学习方法具有更高的准确性和更高的性能。所提出的设计的一个吸引人的特点在于其高性能和快速计算能力，适用于开放领域的潜在实时应用。