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Movement Optimization for a Cyborg Cockroach in a Bounded Space Incorporating Machine Learning
Cockroaches can traverse unknown obstacle-terrain, self-right on the ground and climb above the obstacle. However, they have limited motion, such as less activity in light/bright areas and lower temperatures. Therefore, the movement of the cyborg cockroaches needs to be optimized for the utilization...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
AAAS
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10019993/ https://www.ncbi.nlm.nih.gov/pubmed/36939454 http://dx.doi.org/10.34133/cbsystems.0012 |
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author | Ariyanto, Mochammad Refat, Chowdhury Mohammad Masum Hirao, Kazuyoshi Morishima, Keisuke |
author_facet | Ariyanto, Mochammad Refat, Chowdhury Mohammad Masum Hirao, Kazuyoshi Morishima, Keisuke |
author_sort | Ariyanto, Mochammad |
collection | PubMed |
description | Cockroaches can traverse unknown obstacle-terrain, self-right on the ground and climb above the obstacle. However, they have limited motion, such as less activity in light/bright areas and lower temperatures. Therefore, the movement of the cyborg cockroaches needs to be optimized for the utilization of the cockroach as a cyborg insect. This study aims to increase the search rate and distance traveled by cockroaches and reduce the stop time by utilizing automatic stimulation from machine learning. Multiple machine learning classifiers were applied to classify the offline binary classification of the cockroach movement based on the inertial measuring unit input signals. Ten time-domain features were chosen and applied as the classifier inputs. The highest performance of the classifiers was implemented for the online motion recognition and automatic stimulation provided to the cerci to trigger the free walking motion of the cockroach. A user interface was developed to run multiple computational processes simultaneously in real time such as computer vision, data acquisition, feature extraction, automatic stimulation, and machine learning using a multithreading algorithm. On the basis of the experiment results, we successfully demonstrated that the movement performance of cockroaches was importantly improved by applying machine learning classification and automatic stimulation. This system increased the search rate and traveled distance by 68% and 70%, respectively, while the stop time was reduced by 78%. |
format | Online Article Text |
id | pubmed-10019993 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | AAAS |
record_format | MEDLINE/PubMed |
spelling | pubmed-100199932023-03-17 Movement Optimization for a Cyborg Cockroach in a Bounded Space Incorporating Machine Learning Ariyanto, Mochammad Refat, Chowdhury Mohammad Masum Hirao, Kazuyoshi Morishima, Keisuke Cyborg Bionic Syst Research Article Cockroaches can traverse unknown obstacle-terrain, self-right on the ground and climb above the obstacle. However, they have limited motion, such as less activity in light/bright areas and lower temperatures. Therefore, the movement of the cyborg cockroaches needs to be optimized for the utilization of the cockroach as a cyborg insect. This study aims to increase the search rate and distance traveled by cockroaches and reduce the stop time by utilizing automatic stimulation from machine learning. Multiple machine learning classifiers were applied to classify the offline binary classification of the cockroach movement based on the inertial measuring unit input signals. Ten time-domain features were chosen and applied as the classifier inputs. The highest performance of the classifiers was implemented for the online motion recognition and automatic stimulation provided to the cerci to trigger the free walking motion of the cockroach. A user interface was developed to run multiple computational processes simultaneously in real time such as computer vision, data acquisition, feature extraction, automatic stimulation, and machine learning using a multithreading algorithm. On the basis of the experiment results, we successfully demonstrated that the movement performance of cockroaches was importantly improved by applying machine learning classification and automatic stimulation. This system increased the search rate and traveled distance by 68% and 70%, respectively, while the stop time was reduced by 78%. AAAS 2023-03-15 2023 /pmc/articles/PMC10019993/ /pubmed/36939454 http://dx.doi.org/10.34133/cbsystems.0012 Text en https://creativecommons.org/licenses/by/4.0/Exclusive Licensee Beijing Institute of Technology Press. No claim to original U.S. Government Works. Distributed under a Creative Commons Attribution License 4.0 (CC BY 4.0) (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Research Article Ariyanto, Mochammad Refat, Chowdhury Mohammad Masum Hirao, Kazuyoshi Morishima, Keisuke Movement Optimization for a Cyborg Cockroach in a Bounded Space Incorporating Machine Learning |
title | Movement Optimization for a Cyborg Cockroach in a Bounded Space Incorporating Machine Learning |
title_full | Movement Optimization for a Cyborg Cockroach in a Bounded Space Incorporating Machine Learning |
title_fullStr | Movement Optimization for a Cyborg Cockroach in a Bounded Space Incorporating Machine Learning |
title_full_unstemmed | Movement Optimization for a Cyborg Cockroach in a Bounded Space Incorporating Machine Learning |
title_short | Movement Optimization for a Cyborg Cockroach in a Bounded Space Incorporating Machine Learning |
title_sort | movement optimization for a cyborg cockroach in a bounded space incorporating machine learning |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10019993/ https://www.ncbi.nlm.nih.gov/pubmed/36939454 http://dx.doi.org/10.34133/cbsystems.0012 |
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