Fig. 5

Self-supervised pre-training and reduced data setting. A Pre-training task (performed in [61]): The main component of our harnet model has a Resnet architecture [89]. The Resnet was pre-trained with un-annotated human wrist-worn accelerometer data, which was modified with one of a set of signal transformations (e.g. \(f_0\) = reversal in time). The network was trained to classify which transformation was applied to the original data. B In our harnet model, the input to the pre-trained Resnet was animal bio-logger data, without any modification to sampling rate. The outputs of the Resnet were passed to a recurrent neural network (RNN), which produced the behavior predictions. This full harnet model was then trained as shown in Fig. 3. C In the full data setting, four out of five folds are used to train the model in one-instance of cross validation. In the reduced data setting, only one fold is used for training while the test set is the same. In other words, approximately four times more individuals are included in the train set in the full data setting, than in the reduced data setting. D F1 scores for full data task. harnet frozen does best on five datasets and CRNN does best on three datasets. We omitted the RNN wavelet model from the full data experiments, due to high computational resources required for training, and its poor performance in the reduced data setting. E F1 scores for the reduced data task. harnet frozen does the best on all nine datasets. F Difference in F1 between reduced and full data tasks. For five datasets, harnet frozen shows the smallest decrease in F1 when using reduced data. For precision and recall results, see Figure S22