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Localizing Tortoise Nests by Neural Networks
The goal of this research is to recognize the nest digging activity of tortoises using a device mounted atop the tortoise carapace. The device classifies tortoise movements in order to discriminate between nest digging, and non-digging activity (specifically walking and eating). Accelerometer data w...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2016
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4795789/ https://www.ncbi.nlm.nih.gov/pubmed/26985660 http://dx.doi.org/10.1371/journal.pone.0151168 |
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author | Barbuti, Roberto Chessa, Stefano Micheli, Alessio Pucci, Rita |
author_facet | Barbuti, Roberto Chessa, Stefano Micheli, Alessio Pucci, Rita |
author_sort | Barbuti, Roberto |
collection | PubMed |
description | The goal of this research is to recognize the nest digging activity of tortoises using a device mounted atop the tortoise carapace. The device classifies tortoise movements in order to discriminate between nest digging, and non-digging activity (specifically walking and eating). Accelerometer data was collected from devices attached to the carapace of a number of tortoises during their two-month nesting period. Our system uses an accelerometer and an activity recognition system (ARS) which is modularly structured using an artificial neural network and an output filter. For the purpose of experiment and comparison, and with the aim of minimizing the computational cost, the artificial neural network has been modelled according to three different architectures based on the input delay neural network (IDNN). We show that the ARS can achieve very high accuracy on segments of data sequences, with an extremely small neural network that can be embedded in programmable low power devices. Given that digging is typically a long activity (up to two hours), the application of ARS on data segments can be repeated over time to set up a reliable and efficient system, called Tortoise@, for digging activity recognition. |
format | Online Article Text |
id | pubmed-4795789 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2016 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-47957892016-03-23 Localizing Tortoise Nests by Neural Networks Barbuti, Roberto Chessa, Stefano Micheli, Alessio Pucci, Rita PLoS One Research Article The goal of this research is to recognize the nest digging activity of tortoises using a device mounted atop the tortoise carapace. The device classifies tortoise movements in order to discriminate between nest digging, and non-digging activity (specifically walking and eating). Accelerometer data was collected from devices attached to the carapace of a number of tortoises during their two-month nesting period. Our system uses an accelerometer and an activity recognition system (ARS) which is modularly structured using an artificial neural network and an output filter. For the purpose of experiment and comparison, and with the aim of minimizing the computational cost, the artificial neural network has been modelled according to three different architectures based on the input delay neural network (IDNN). We show that the ARS can achieve very high accuracy on segments of data sequences, with an extremely small neural network that can be embedded in programmable low power devices. Given that digging is typically a long activity (up to two hours), the application of ARS on data segments can be repeated over time to set up a reliable and efficient system, called Tortoise@, for digging activity recognition. Public Library of Science 2016-03-17 /pmc/articles/PMC4795789/ /pubmed/26985660 http://dx.doi.org/10.1371/journal.pone.0151168 Text en © 2016 Barbuti et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article Barbuti, Roberto Chessa, Stefano Micheli, Alessio Pucci, Rita Localizing Tortoise Nests by Neural Networks |
title | Localizing Tortoise Nests by Neural Networks |
title_full | Localizing Tortoise Nests by Neural Networks |
title_fullStr | Localizing Tortoise Nests by Neural Networks |
title_full_unstemmed | Localizing Tortoise Nests by Neural Networks |
title_short | Localizing Tortoise Nests by Neural Networks |
title_sort | localizing tortoise nests by neural networks |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4795789/ https://www.ncbi.nlm.nih.gov/pubmed/26985660 http://dx.doi.org/10.1371/journal.pone.0151168 |
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