Effectiveness of gyroscopes and triaxial accelerometers paired with deep learning algorithms in detecting dairy camel behavior

Nowadays, the behavior of dairy camels in intensive systems has received little attention. This study is the first to use wearable sensors to predict camel behavior, filling a knowledge gap by providing information into their activities. A novel system using triaxial accelerometer and gyroscope sensor was developed to monitor and predict the behavior of six female Maghrebi dairy camels. Using a 10-second time window for data collection, this research effectively distinguishes between key behaviors such as feeding, ruminating, resting, and walking. Various deep learning techniques, including convolutional neural networks combined with long short-term memory (ConvLSTM), dense layer convolutional neural networks, and standalone long short-term memory (LSTM) networks, were used to analyze the data. Results indicate that, under our conditions, camels spent most of the study time feeding, with 33134 occurrences (444 minutes), followed by ruminating 11611 times (156 minutes) and resting 10487...