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Ethoflow: Computer Vision and Artificial Intelligence-Based Software for Automatic Behavior Analysis
Manual monitoring of animal behavior is time-consuming and prone to bias. An alternative to such limitations is using computational resources in behavioral assessments, such as tracking systems, to facilitate accurate and long-term evaluations. There is a demand for robust software that addresses an...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8124799/ https://www.ncbi.nlm.nih.gov/pubmed/34067084 http://dx.doi.org/10.3390/s21093237 |
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author | Bernardes, Rodrigo Cupertino Lima, Maria Augusta Pereira Guedes, Raul Narciso Carvalho da Silva, Clíssia Barboza Martins, Gustavo Ferreira |
author_facet | Bernardes, Rodrigo Cupertino Lima, Maria Augusta Pereira Guedes, Raul Narciso Carvalho da Silva, Clíssia Barboza Martins, Gustavo Ferreira |
author_sort | Bernardes, Rodrigo Cupertino |
collection | PubMed |
description | Manual monitoring of animal behavior is time-consuming and prone to bias. An alternative to such limitations is using computational resources in behavioral assessments, such as tracking systems, to facilitate accurate and long-term evaluations. There is a demand for robust software that addresses analysis in heterogeneous environments (such as in field conditions) and evaluates multiple individuals in groups while maintaining their identities. The Ethoflow software was developed using computer vision and artificial intelligence (AI) tools to monitor various behavioral parameters automatically. An object detection algorithm based on instance segmentation was implemented, allowing behavior monitoring in the field under heterogeneous environments. Moreover, a convolutional neural network was implemented to assess complex behaviors expanding behavior analyses’ possibilities. The heuristics used to generate training data for the AI models automatically are described, and the models trained with these datasets exhibited high accuracy in detecting individuals in heterogeneous environments and assessing complex behavior. Ethoflow was employed for kinematic assessments and to detect trophallaxis in social bees. The software was developed in desktop applications and had a graphical user interface. In the Ethoflow algorithm, the processing with AI is separate from the other modules, facilitating measurements on an ordinary computer and complex behavior assessing on machines with graphics processing units. Ethoflow is a useful support tool for applications in biology and related fields. |
format | Online Article Text |
id | pubmed-8124799 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-81247992021-05-17 Ethoflow: Computer Vision and Artificial Intelligence-Based Software for Automatic Behavior Analysis Bernardes, Rodrigo Cupertino Lima, Maria Augusta Pereira Guedes, Raul Narciso Carvalho da Silva, Clíssia Barboza Martins, Gustavo Ferreira Sensors (Basel) Article Manual monitoring of animal behavior is time-consuming and prone to bias. An alternative to such limitations is using computational resources in behavioral assessments, such as tracking systems, to facilitate accurate and long-term evaluations. There is a demand for robust software that addresses analysis in heterogeneous environments (such as in field conditions) and evaluates multiple individuals in groups while maintaining their identities. The Ethoflow software was developed using computer vision and artificial intelligence (AI) tools to monitor various behavioral parameters automatically. An object detection algorithm based on instance segmentation was implemented, allowing behavior monitoring in the field under heterogeneous environments. Moreover, a convolutional neural network was implemented to assess complex behaviors expanding behavior analyses’ possibilities. The heuristics used to generate training data for the AI models automatically are described, and the models trained with these datasets exhibited high accuracy in detecting individuals in heterogeneous environments and assessing complex behavior. Ethoflow was employed for kinematic assessments and to detect trophallaxis in social bees. The software was developed in desktop applications and had a graphical user interface. In the Ethoflow algorithm, the processing with AI is separate from the other modules, facilitating measurements on an ordinary computer and complex behavior assessing on machines with graphics processing units. Ethoflow is a useful support tool for applications in biology and related fields. MDPI 2021-05-07 /pmc/articles/PMC8124799/ /pubmed/34067084 http://dx.doi.org/10.3390/s21093237 Text en © 2021 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Bernardes, Rodrigo Cupertino Lima, Maria Augusta Pereira Guedes, Raul Narciso Carvalho da Silva, Clíssia Barboza Martins, Gustavo Ferreira Ethoflow: Computer Vision and Artificial Intelligence-Based Software for Automatic Behavior Analysis |
title | Ethoflow: Computer Vision and Artificial Intelligence-Based Software for Automatic Behavior Analysis |
title_full | Ethoflow: Computer Vision and Artificial Intelligence-Based Software for Automatic Behavior Analysis |
title_fullStr | Ethoflow: Computer Vision and Artificial Intelligence-Based Software for Automatic Behavior Analysis |
title_full_unstemmed | Ethoflow: Computer Vision and Artificial Intelligence-Based Software for Automatic Behavior Analysis |
title_short | Ethoflow: Computer Vision and Artificial Intelligence-Based Software for Automatic Behavior Analysis |
title_sort | ethoflow: computer vision and artificial intelligence-based software for automatic behavior analysis |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8124799/ https://www.ncbi.nlm.nih.gov/pubmed/34067084 http://dx.doi.org/10.3390/s21093237 |
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