DI-UMONS : Dépôt institutionnel de l’université de Mons

Recherche transversale
(titres de publication, de périodique et noms de colloque inclus)
2017-06-27 - Article/Dans un journal avec peer-review - Anglais - 7 page(s)

Debauche Olivier , Mahmoudi Said , Andriamandroso Andriamasinoro Lalaina Herinaina, Manneback Pierre , Bindelle Jérôme, Lebeau Frédéric, "Web-based cattle behavior service for researchers based on the smartphone inertial central" in Procedia Computer Science, 110, 110-116

  • Edition : Elsevier (Netherlands)
  • Codes CREF : Informatique générale (DI1162)
  • Unités de recherche UMONS : Informatique (F114)
  • Instituts UMONS : Institut de Recherche en Technologies de l’Information et Sciences de l’Informatique (InforTech), Institut NUMEDIART pour les Technologies des Arts Numériques (Numédiart)
Texte intégral :

Abstract(s) :

(Anglais) Smartphones, particularly iPhones, can be relevant instruments for researchers in animal behavior because they are readily available on the planet, contain many sensors and require no hardware development. They are equipped with high performance inertial measurement units (IMU) and absolute positioning systems analyzing users’ movements, but they can easily be diverted to analyze likewise the behaviors of domestic animals such as cattle. The study of animal behavior using smartphones requires the storage of many high frequency variables from a large number of individuals and their processing through various relevant variables combinations for modeling and decision-making. Transferring, storing, treating and sharing such an amount of data is a big challenge. In this paper, a lambda cloud architecture and a scientific sharing platform used to archive and process highfrequency data are proposed. An application to the study of cattle behavior on pasture on the basis of the data recorded with the IMU of iPhones 4S is exemplified. The package comes also with a web interface to encode the actual behavior observed on videos and to synchronize observations with the sensor signals. Finally, the use of fog computing on the iPhone reduced by 42% on average the size of the raw data by eliminating redundancies.

Identifiants :
  • DOI : 10.1016/j.procs.2017.06.127

Mots-clés :
  • (Anglais) precision livestock farming
  • (Anglais) webservice
  • (Anglais) intertial unit
  • (Anglais) smart breeding
  • (Anglais) classification algorithms
  • (Anglais) Internet of things
  • (Anglais) animal behavior
  • (Anglais) database
  • (Anglais) smart agriculture