Fundamental challenges faced by real-time animal activity recognition include variation in motion data due to changing sensor orientations, numerous features, and energy and processing constraints of animal tags. This paper aims at finding small optimal feature sets that are lightweight and robust to the sensor's orientation. Our approach comprises four main steps. First, 3D feature vectors are selected since they are theoretically independent of orientation. Second, the least interesting features are suppressed to speed up computation and increase robustness against overfitting. Third, the features are further selected through an embedded method, which selects features through simultaneous feature selection and classification. Finally, feature sets are optimized through 10-fold cross-validation. We collected real-world data through multiple sensors around the neck of five goats. The results show that activities can be accurately recognized using only accelerometer data and a few lightweight features. Additionally, we show that the performance is robust to sensor orientation and position. A simple Naive Bayes classifier using only a single feature achieved an accuracy of 94 % with our empirical dataset. Moreover, our optimal feature set yielded an average of 94 % accuracy when applied with six other classifiers. This work supports embedded, real-time, energy-efficient, and robust activity recognition for animals.
- Animal Activity Recognition, Decision Tree, Embedded Systems, Machine Learning, Naive Bayes, Sensor Orientation