Wearable Inertial Sensor-Based Limb Lameness Detection and Pose Estimation for Horses

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 detec...

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Bibliographic Details
Published in:IEEE transactions on automation science and engineering 2022-07, Vol.19 (3), p.1-15
Main Authors: Yigit, Tarik, Han, Feng, Rankins, Ellen, Yi, Jingang, McKeever, Kenneth H., Malinowski, Karyn
Format: Article
Language:English
Subjects:
Accelerometers
Accuracy
Agriculture
Artificial neural networks
Automation
Classifiers
Dynamic models
Equine gait analysis
Farming
Feature extraction
Gait
Gaussian process
Horses
inertial measurement units
Inertial sensing devices
lameness detection
Legged locomotion
Livestock
Locomotion
Machine learning
Manifolds (mathematics)
Multilayers
Neural networks
Pose estimation
Real-time systems
Recurrent neural networks
Sensors
Support vector machines
Wearable technology
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Summary: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.
ISSN:1545-5955
1558-3783
DOI:10.1109/TASE.2022.3157793