A CNN-Based Animal Behavior Recognition Algorithm for Wearable Devices

In order to achieve automatic behavioral monitoring of farming animals, this article proposes a convolutional neural network (CNN)-based behavioral recognition method for lactating sows. The behavioral data streams of lactating sows are collected by wearable sensors embedded with a 3-D accelerometer and a 3-D gyroscope and used to recognize six types of behaviors, including movement, drinking, eating, nursing, sleeping, and lying. Among these behaviors, nursing, lying, and sleeping can be classified as similar static behaviors. Based on the action images constructed with sensor data streams, CNNs are leveraged for the purpose of distinguishing static behaviors of the lactating sow. To address the problem of insufficient training data, we use the data augmentation technique. Experimental results verify the data augmentation method's effectiveness and show that our proposed behavioral monitoring method has greater advantages in terms of accuracy than traditional machine learning methods. The research results have implications for behavioral monitoring and health assessment of lactating sows.

Automated summary

This article proposes a CNN-based behavioral recognition method for automatic monitoring of lactating sows. The behavior data streams are collected through wearable sensors and used to recognize six types of behaviors. By leveraging CNNs and using data augmentation, the proposed method achieves higher accuracy in distinguishing static behaviors compared to traditional machine learning methods. The research results have important implications for behavioral monitoring and health assessment of lactating sows.