From Single to Deep Learning and Hybrid Ensemble Models for Recognition of Dog Motion States

Dog activities recognition, especially dog motion status recognition, is an active research area. Although several machine learning and deep learning approaches have been used for dog motion states recognition, the use of ensemble learning methods is rather missing, as well as a comparison with deep learning ones. This paper focuses on the use of deep learning neural networks and ensemble classifiers in recognizing dog motion states and their comparison. A dataset from the Kaggle database, which includes measures by accelerometer and gyroscope and concerns seven dog motion states (galloping, sitting, standing, trotting, walking, lying on chest, and sniffing), was used for our experiments. Gaussian Naive Bayes, Decision Tree, k-Nearest Neighbors (kNN), Random Forest, a Bagging Tree-Based Classifier, a Stacking Classifier, a Compound Stacking Model (CSM), a Convolutional Neural Network (CNN), a Recurrent Neural Network (RNN), and a Hybrid Cascading Model (HCM) were used in our experiments. Results showed a 1.78% superiority in accuracy (92.64% vs. 90.86%) of deep learning (RNN) vs. stacking (CSTAM) best classifier, but at the cost of larger complexity and training time for the deep learning classifier, which makes ensemble techniques still attractive. Finally, HCM gave the best result (96.82% accuracy).