Animal-attached accelerometers have been widely used to monitor species that are difficult to observe, alongside the use of machine learning to identify behaviours from the obtained sequences. Artificial neural networks are powerful supervised learning algorithms that are based on deep learning and have been poorly exploited in movement ecology. Recently, the availability of sophisticated algorithmic architectures via open source libraries facilitates their use. In this study, we adapt a fully convolutional neural network that was originally developed for biomedical 3D image segmentation: the V-net. We test it on a labelled dataset collected from animal-borne video recorders combined with multi-sensors (accelerometers, gyroscopes and depth recorders) deployed on free-ranging immature green turtles (Chelonia mydas). The proposed model, fitted for 1D data, is able to predict six behavioural categories for green turtles with an AUC score of 88%. It shows a high ability to detect rare behaviours with low discriminative signals such as Feeding and Scratching. With a precision down to one centisecond, the V-net circumvents the segmentation process. We also show that the gyroscope is more informative than the accelerometer in identifying sea turtle behaviours and that the V-net is not able to discriminate Feeding from the raw data of accelerometer alone. However, human expertise can help to correct it with precise and adapted pre-processing. Thus, diverted from its initial purpose and tested on sea turtle, the V-net is a very efficient method of behavioural identification that should be easily generalized to a wide range of species. It could lead to considerable progress in remote accelerometric monitoring and help to understand the ecology of the species that are difficult to observe. Furthermore, as the model is light, there is also a huge potential to implement a trained V-net in satellite-relay data tag to remotely predict the expressed behaviours almost instantly.

与动物相连的加速度计已广泛用于监视难以观察的物种，此外还使用机器学习从获得的序列中识别行为。人工神经网络是强大的监督学习算法，它是基于深度学习的，在运动生态学中没有得到很好的利用。最近，通过开放源代码库提供的复杂算法体系结构促进了它们的使用。在这项研究中，我们采用了最初用于生物医学3D图像分割的全卷积神经网络：V-net。我们在从动物传播的录像机和部署在自由放养的未成熟绿海龟（Chelonia mydas）。所提出的模型适合一维数据，能够预测AUC得分为88％的绿海龟的六种行为类别。它具有很高的检测能力，能够以低判别信号（例如进纸和刮擦）检测稀有行为。V-net的精度低至1厘秒，可绕开分段过程。我们还表明，陀螺仪在识别海龟行为方面比加速度计提供更多信息，并且V-net无法区分进食仅从加速度计的原始数据。但是，人类的专业知识可以通过精确和适应性的预处理来纠正它。因此，从最初的用途转向并在海龟上进行了测试，V-net是一种非常有效的行为识别方法，应该容易地推广到广泛的物种中。这可能会在远程加速度监测中带来可观的进步，并有助于了解难以观察的物种的生态。此外，由于该模型比较轻巧，因此在卫星中继数据标签中实施训练有素的V-net几乎可以立即远程预测所表达的行为，也具有巨大的潜力。