A survey of machine learning approaches in animal behaviour

Natasa Kleanthous, Abir Jaafar Hussain, Wasiq Khan, Jennifer Sneddon, Ahmed Al-Shamma'a, Panos Liatsis

Research output: Contribution to journalArticlepeer-review

20 Scopus citations

Abstract

Animal activity recognition is an important topic that facilitates understanding of animal behavior that is useful for analyzing and classifying their wellbeing. Research studies have been reporting the use of animal activity as an effective indicator of their health state. This survey focuses on recent advancements in machine intelligence utilizing wearable devices for sheep activity recognition. We summarise existing works focusing on various types of sensors used in agricultural sheep activity recognition. Furthermore, data segmentation methods used in each study, followed by the potential recommendations on window size and sample rate selection are addressed in detail. Finally, we present the features being identified as significant along with an overview of machine learning algorithms used in the domain of sheep activity recognition using accelerometer data.

Original languageBritish English
Pages (from-to)442-463
Number of pages22
JournalNeurocomputing
Volume491
DOIs
StatePublished - 28 Jun 2022

Keywords

  • Deep learning
  • Feature selection
  • Machine learning
  • Multi-sensor activity
  • Sheep activity recognition
  • Sheep activity survey

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