Accelerometer, gyroscope and pressure data associated with behaviors of free-ranging hawksbill sea turtles (Chelonia mydas)

Data accompanying the paper: Jeantet, L., Vigon, V., Geiger, S., & Chevallier, D. (2021). Fully convolutional neural network: A solution to infer animal behaviours from multi-sensor data. Ecological Modelling, 450, 109555.. doi : https://doi.org/10.1016/j.ecolmodel.2021.109555

In this paper we developped a fully convolutional network, the V-Net, to automatically identify the behaviors of green turtle from acceleration, gyroscope, depth sensor data. With minimal preprocessing, we obtained a F1-score of 81.1% and a Global accuracy of 97.2%. 

Associated Github with the V-Net script : https://github.com/jeantetlorene/Vnet_seaturtle_behavior

The dataset comprised the raw acceleration, gyroscope and depth sequence of 13 free-ranging green turtles associated with the behaviors. The indiviuals were equipped with a on-board video recorder combined with an accelerometer, gyroscope, magnetometer and luminosity, temperature and depth sensors using four suction cups and an automatic release system over a two-day periods (see Jeantet et al. 2020 for details and the associated article). The accelerometer, gyroscope, magnetometer recorded at 20 Hz and the pressure, temperature and luminosity sensors at 1 Hz. The cameras were programmed to record until nightfall (6 pm) and resume at daybreak (6 am). The magnetometer, luminosity and temperature data are not provided in this dataset. 

For each individual, the data collected by the devices was correlated with observed behaviors from video recordings. Unlabeled sequences, primarily night recordings, were excluded, resulting in the creation of one file per day of deployment for each individual. A total of 46 behaviors were observed and are described in detail in Jeantet et al. (2020). The labels for these behaviors are found in the column "beh." The behaviors were grouped into six main categories: Breathing, Feeding, Gliding, Resting, Scratching, and Swimming. Any other observed behavior was categorized as Other. The associated labels for the categories can be found in the column "beh_merge."

To process the depth data and increase the sampling rate to 20 Hz, we used a linear interpolation technique. We called this new variable "Pressure_corr". Additionally, we calculated the pressure difference ("Pressure_diff") between each measuring point (originally at 1 Hz).

"In total, the green turtle dataset contained 68.6 hours of labelled sequences from 13 individuals (approximately 5.29 hours per individual, max = 14.67 hours, min = 0.96 hours, standard deviation = 3.39 hours). The predominant behavior observed in the videos was Resting, totaling over 34.3 hours, followed by Swimming and Breathing, with 22.3 hours and 5.7 hours, respectively. The other behaviors were expressed in minority (Gliding: 2.3 hours, Feeding: 1.8 hours, Scratching: 1.2 hours and Other: 1 hour). "

The folder contains 16 Python matrices, each with 11 columns (AccX, AccY, AccZ, GyrX, GyrY, GyrZ, Depth, beh, beh_merge, Pressure_corr, Pressur_diff) and a number of rows corresponding to the deployment duration. The title of each file indicates the camera number used (CC-07-XX) and the deployment day (DD-MM-YYYY), with an additional number if the file was split due to unlabeled sequences.

The folder also contains two dictionaries (behInd_to_behName, behName_to_behInd) that specify the behaviors associated with each number used as a label in the "beh" column. Two dictionaries (behInd_to_behName_cat, behName_to_behInd_cat) that specify the behavioral categories associated with each number used as a label in the "beh_merge" column. Additionally, there is a dictionary (dico_info) that provides the names of the matrix columns and the frequence of recording.

Please cite this dataset as : 

Jeantet, L., Planas-Bielsa, V., Benhamou, S., Geiger, S., Martin, J., Siegwalt, F., Lelong, P., Gresser, J., Etienne, D., Hielard, G., Arque, A., Regis, S., Lecerf, N., Frouin, C., Benhalilou, A., Murgale, C., Maillet, T., Andreani, L., Campistron, G., … Chevallier, D. (2024). Accelerometer, gyroscope and pressure data associated with behaviors of free-ranging hawksbill sea turtles (Chelonia mydas) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.11643602