Classifying animal behavior from accelerometry data via recurrent neural networks

We study the classification of animal behavior from accelerometry data using various recurrent neural network (RNN) models. RNNs have extensively been employed to classify time-series data in various applications. However, their utilization for classifying animal behavior from wearable sensor data, particularly accelerometry data, as well as the underlying accuracy-complexity trade-offs is rather under-explored. We use four triaxial accelerometry datasets acquired from grazing cattle via collar tags or ear tags to evaluate the classification performance and complexity of the considered RNN models, which feature long short-time memory (LSTM) or gated recurrent unit (GRU) architectures with varying depths and widths. In the evaluations, we also include two state-of-the-art convolutional neural network (CNN)-based time-series classification models. The results show that the considered RNN-based models can achieve similar or higher animal behavior classification accuracy compared to the CNN-based models while having smaller computational and memory requirements. We also observe that the GRU-based models generally outperform the LSTM-based ones in terms of classification accuracy despite being less complex. A single-layer uni-directional GRU model with 64 hidden units appears to offer a good balance between accuracy and complexity making it suitable for implementation on edge/embedded devices.