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Sensors (Basel, Switzerland)2023; 23(12); 5732; doi: 10.3390/s23125732

Machine Learning-Based Sensor Data Fusion for Animal Monitoring: Scoping Review.

Abstract: The development of technology, such as the Internet of Things and artificial intelligence, has significantly advanced many fields of study. Animal research is no exception, as these technologies have enabled data collection through various sensing devices. Advanced computer systems equipped with artificial intelligence capabilities can process these data, allowing researchers to identify significant behaviors related to the detection of illnesses, discerning the emotional state of the animals, and even recognizing individual animal identities. This review includes articles in the English language published between 2011 and 2022. A total of 263 articles were retrieved, and after applying inclusion criteria, only 23 were deemed eligible for analysis. Sensor fusion algorithms were categorized into three levels: Raw or low (26%), Feature or medium (39%), and Decision or high (34%). Most articles focused on posture and activity detection, and the target species were primarily cows (32%) and horses (12%) in the three levels of fusion. The accelerometer was present at all levels. The findings indicate that the study of sensor fusion applied to animals is still in its early stages and has yet to be fully explored. There is an opportunity to research the use of sensor fusion for combining movement data with biometric sensors to develop animal welfare applications. Overall, the integration of sensor fusion and machine learning algorithms can provide a more in-depth understanding of animal behavior and contribute to better animal welfare, production efficiency, and conservation efforts.
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Publication Date: 2023-06-20 PubMed ID: 37420896PubMed Central: PMC10305307DOI: 10.3390/s23125732Google Scholar: Lookup
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Summary

This research summary has been generated with artificial intelligence and may contain errors and omissions. Refer to the original study to confirm details provided. Submit correction.

This research review analyses how modern technologies like artificial intelligence and Internet of Things, which facilitate data collection through sensors, have been used in animal research. It categorizes and analyzes the application of sensor fusion algorithms in understanding animal behavior for improved welfare, productivity and conservation efforts.

Introduction

  • The research study on hand is a specialized ‘scoping review’, focusing on the applications of the fields of artificial intelligence and the Internet of Things (IoT) in animal conservation.
  • The primary technologies considered in the review are sensing devices enabled with AI and IoT, that can process large volumes of data to extract significant insights about animal behavior.
  • The focus is on how these technologies are used to understand behaviors related to illness detection, interpreting the emotional state of animals, and even recognizing individual animals.

Methodology

  • The study included English language articles published in a time frame of over a decade, from 2011 to 2022.
  • Out of 263 gathered articles, only 23 met the set inclusion criteria and were chosen for detailed analysis.

Classification and Analysis of Sensor Fusion Algorithms

  • The sensor fusion algorithms extracted from the eligible articles were categorized into three levels: Raw or low-level fusion (26%), Feature or medium-level fusion (39%), and Decision or high-level fusion (34%).
  • The algorithms were majorly used for detection of posture and activity in animals with cows (32%) and horses (12%) being the main target species in the three levels of fusion.
  • The accelerometer, a device that measures proper acceleration, was a common feature across all levels of fusion and data collection.

Conclusion and Future Scope

  • The review concludes that the exploration of sensor fusion in animal research is still in its initial stage and is ripe for further exploration and development.
  • There is a substantial potential for combining movement data with biometrics to develop comprehensive animal welfare applications.
  • The integration of sensor fusion and machine learning algorithms can significantly enhance the understanding of animal behavior, leading to beneficial outcomes for animal welfare, increasing production efficiency, and bolstering conservation efforts.

Cite This Article

APA
Aguilar-Lazcano CA, Espinosa-Curiel IE, Ríos-Martínez JA, Madera-Ramírez FA, Pérez-Espinosa H. (2023). Machine Learning-Based Sensor Data Fusion for Animal Monitoring: Scoping Review. Sensors (Basel), 23(12), 5732. https://doi.org/10.3390/s23125732

Publication

Sensors (Basel, Switzerland)
ISSN: 1424-8220
NlmUniqueID: 101204366
Country: Switzerland
Language: English
Volume: 23
Issue: 12
PII: 5732

Researcher Affiliations

Aguilar-Lazcano, Carlos Alberto
  • CICESE-UT3, Tepic 63173, Mexico.
Espinosa-Curiel, Ismael Edrein
  • CICESE-UT3, Tepic 63173, Mexico.
Ríos-Martínez, Jorge Alberto
  • Computer Science Department, Faculty of Mathematics, Autonomous University of Yucatan, Merida 97000, Mexico.
Madera-Ramírez, Francisco Alejandro
  • Computer Science Department, Faculty of Mathematics, Autonomous University of Yucatan, Merida 97000, Mexico.
Pérez-Espinosa, Humberto
  • CICESE-UT3, Tepic 63173, Mexico.

MeSH Terms

  • Female
  • Cattle
  • Animals
  • Horses
  • Artificial Intelligence
  • Machine Learning
  • Algorithms
  • Movement
  • Biometry

Conflict of Interest Statement

The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.

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