Micro Activities Recognition in Uncontrolled Environments

Deep learning has proven to be very useful for the image understanding in efficient manners. Assembly of complex machines is very common in industries. The assembly of automated teller machines (ATM) is one of the examples. There exist deep learning models which monitor and control the assembly proc...

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Autores principales:	Ali Abbas, Michael Haslgrübler, Abdul Mannan Dogar, Alois Ferscha
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	assembly process activity recognition deep learning neural networks uncontrolled real time environment Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999
Acceso en línea:	https://doaj.org/article/091faf8ac04b4983a2d1a49da9371192
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id	oai:doaj.org-article:091faf8ac04b4983a2d1a49da9371192
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spelling	oai:doaj.org-article:091faf8ac04b4983a2d1a49da93711922021-11-11T15:21:26ZMicro Activities Recognition in Uncontrolled Environments10.3390/app1121103272076-3417https://doaj.org/article/091faf8ac04b4983a2d1a49da93711922021-11-01T00:00:00Zhttps://www.mdpi.com/2076-3417/11/21/10327https://doaj.org/toc/2076-3417Deep learning has proven to be very useful for the image understanding in efficient manners. Assembly of complex machines is very common in industries. The assembly of automated teller machines (ATM) is one of the examples. There exist deep learning models which monitor and control the assembly process. To the best of our knowledge, there exists no deep learning models for real environments where we have no control over the working style of workers and the sequence of assembly process. In this paper, we presented a modified deep learning model to control the assembly process in a real-world environment. For this study, we have a dataset which was generated in a real-world uncontrolled environment. During the dataset generation, we did not have any control over the sequence of assembly steps. We applied four different states of the art deep learning models to control the assembly of ATM. Due to the nature of uncontrolled environment dataset, we modified the deep learning models to fit for the task. We not only control the sequence, our proposed model will give feedback in case of any missing step in the required workflow. The contributions of this research are accurate anomaly detection in the assembly process in a real environment, modifications in existing deep learning models according to the nature of the data and normalization of the uncontrolled data for the training of deep learning model. The results show that we can generalize and control the sequence of assembly steps, because even in an uncontrolled environment, there are some specific activities, which are repeated over time. If we can recognize and map the micro activities to macro activities, then we can successfully monitor and optimize the assembly process.Ali AbbasMichael HaslgrüblerAbdul Mannan DogarAlois FerschaMDPI AGarticleassembly processactivity recognitiondeep learningneural networksuncontrolled real time environmentTechnologyTEngineering (General). Civil engineering (General)TA1-2040Biology (General)QH301-705.5PhysicsQC1-999ChemistryQD1-999ENApplied Sciences, Vol 11, Iss 10327, p 10327 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	assembly process activity recognition deep learning neural networks uncontrolled real time environment Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999
spellingShingle	assembly process activity recognition deep learning neural networks uncontrolled real time environment Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999 Ali Abbas Michael Haslgrübler Abdul Mannan Dogar Alois Ferscha Micro Activities Recognition in Uncontrolled Environments
description	Deep learning has proven to be very useful for the image understanding in efficient manners. Assembly of complex machines is very common in industries. The assembly of automated teller machines (ATM) is one of the examples. There exist deep learning models which monitor and control the assembly process. To the best of our knowledge, there exists no deep learning models for real environments where we have no control over the working style of workers and the sequence of assembly process. In this paper, we presented a modified deep learning model to control the assembly process in a real-world environment. For this study, we have a dataset which was generated in a real-world uncontrolled environment. During the dataset generation, we did not have any control over the sequence of assembly steps. We applied four different states of the art deep learning models to control the assembly of ATM. Due to the nature of uncontrolled environment dataset, we modified the deep learning models to fit for the task. We not only control the sequence, our proposed model will give feedback in case of any missing step in the required workflow. The contributions of this research are accurate anomaly detection in the assembly process in a real environment, modifications in existing deep learning models according to the nature of the data and normalization of the uncontrolled data for the training of deep learning model. The results show that we can generalize and control the sequence of assembly steps, because even in an uncontrolled environment, there are some specific activities, which are repeated over time. If we can recognize and map the micro activities to macro activities, then we can successfully monitor and optimize the assembly process.
format	article
author	Ali Abbas Michael Haslgrübler Abdul Mannan Dogar Alois Ferscha
author_facet	Ali Abbas Michael Haslgrübler Abdul Mannan Dogar Alois Ferscha
author_sort	Ali Abbas
title	Micro Activities Recognition in Uncontrolled Environments
title_short	Micro Activities Recognition in Uncontrolled Environments
title_full	Micro Activities Recognition in Uncontrolled Environments
title_fullStr	Micro Activities Recognition in Uncontrolled Environments
title_full_unstemmed	Micro Activities Recognition in Uncontrolled Environments
title_sort	micro activities recognition in uncontrolled environments
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/091faf8ac04b4983a2d1a49da9371192
work_keys_str_mv	AT aliabbas microactivitiesrecognitioninuncontrolledenvironments AT michaelhaslgrubler microactivitiesrecognitioninuncontrolledenvironments AT abdulmannandogar microactivitiesrecognitioninuncontrolledenvironments AT aloisferscha microactivitiesrecognitioninuncontrolledenvironments
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Micro Activities Recognition in Uncontrolled Environments

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