Data Augmentation for Inertial Sensor Data in CNNs for Cattle Behavior Classification
Chao Li, Korkut Kaan Tokgöz, Masamoto Fukawa, Jim Bartels, T. Ohashi, Ken’ichi Takeda, Hiroyuki Ito
Abstract
Cattle behavior monitoring is critical for understanding cattle welfare and health status. One of the most powerful and cost-effective monitoring methods is a neural network-based monitoring system that analyzes time series data from inertial sensors attached to cows. However, while deep learning has achieved many successes in pattern recognition, large-scale datasets are often required. When given a limited number of data, data augmentation is an extremely useful and low-cost preprocessing step for neural network-based systems. Data augmentation for inertial sensor data, however, has yet to be thoroughly investigated. This letter proposes several inertial sensor data augmentation methods in a manner that fits the characteristics of cattle behavioral data. The proposed approaches are applied to the task of cattle behavior classification with convolutional neural networks, which is challenging given limited data. The classification performance increases from 83.07 to 94.43% with appropriate augmentation steps. In conclusion, the data augmentation approaches presented here can help to improve deep learning performance regarding cattle behavior classification and decrease the overall system cost stemming from data acquisition and labeling.