Empirical relationships between algorithmic SDA-M-based memory assessments and human errors in manual assembly tasks

Abstract The majority of manufacturing tasks are still performed by human workers, and this will probably continue to be the case in many industry 4.0 settings that aim at highly customized products and small lot sizes. Technical systems could assist on-the-job training and execution of these manual...

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Autores principales:	Benjamin Strenge, Thomas Schack
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/00dfa270711b476db47e25282630360a
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spelling	oai:doaj.org-article:00dfa270711b476db47e25282630360a2021-12-02T15:37:58ZEmpirical relationships between algorithmic SDA-M-based memory assessments and human errors in manual assembly tasks10.1038/s41598-021-88921-12045-2322https://doaj.org/article/00dfa270711b476db47e25282630360a2021-05-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-88921-1https://doaj.org/toc/2045-2322Abstract The majority of manufacturing tasks are still performed by human workers, and this will probably continue to be the case in many industry 4.0 settings that aim at highly customized products and small lot sizes. Technical systems could assist on-the-job training and execution of these manual assembly processes, using augmented reality and other means, by properly treating and supporting workers’ cognitive resources. Recent algorithmic advancements automatized the assessment of task-related mental representation structures based on SDA-M, which enables technical systems to anticipate mistakes and provide corresponding user-specific assistance. Two studies have empirically investigated the relations between algorithmic assessments of individual memory structures and the occurrences of human errors in different assembly tasks. Hereby theoretical assumptions of the automatized SDA-M assessment approaches were deliberately violated in realistic ways to evaluate the practical applicability of these approaches. Substantial but imperfect correspondences were found between task-related mental representation structures and actual performances with sensitivity and specificity values ranging from 0.63 to 0.72, accompanied by prediction accuracies that were highly significant above chance level.Benjamin StrengeThomas SchackNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-12 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Benjamin Strenge Thomas Schack Empirical relationships between algorithmic SDA-M-based memory assessments and human errors in manual assembly tasks
description	Abstract The majority of manufacturing tasks are still performed by human workers, and this will probably continue to be the case in many industry 4.0 settings that aim at highly customized products and small lot sizes. Technical systems could assist on-the-job training and execution of these manual assembly processes, using augmented reality and other means, by properly treating and supporting workers’ cognitive resources. Recent algorithmic advancements automatized the assessment of task-related mental representation structures based on SDA-M, which enables technical systems to anticipate mistakes and provide corresponding user-specific assistance. Two studies have empirically investigated the relations between algorithmic assessments of individual memory structures and the occurrences of human errors in different assembly tasks. Hereby theoretical assumptions of the automatized SDA-M assessment approaches were deliberately violated in realistic ways to evaluate the practical applicability of these approaches. Substantial but imperfect correspondences were found between task-related mental representation structures and actual performances with sensitivity and specificity values ranging from 0.63 to 0.72, accompanied by prediction accuracies that were highly significant above chance level.
format	article
author	Benjamin Strenge Thomas Schack
author_facet	Benjamin Strenge Thomas Schack
author_sort	Benjamin Strenge
title	Empirical relationships between algorithmic SDA-M-based memory assessments and human errors in manual assembly tasks
title_short	Empirical relationships between algorithmic SDA-M-based memory assessments and human errors in manual assembly tasks
title_full	Empirical relationships between algorithmic SDA-M-based memory assessments and human errors in manual assembly tasks
title_fullStr	Empirical relationships between algorithmic SDA-M-based memory assessments and human errors in manual assembly tasks
title_full_unstemmed	Empirical relationships between algorithmic SDA-M-based memory assessments and human errors in manual assembly tasks
title_sort	empirical relationships between algorithmic sda-m-based memory assessments and human errors in manual assembly tasks
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/00dfa270711b476db47e25282630360a
work_keys_str_mv	AT benjaminstrenge empiricalrelationshipsbetweenalgorithmicsdambasedmemoryassessmentsandhumanerrorsinmanualassemblytasks AT thomasschack empiricalrelationshipsbetweenalgorithmicsdambasedmemoryassessmentsandhumanerrorsinmanualassemblytasks
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Empirical relationships between algorithmic SDA-M-based memory assessments and human errors in manual assembly tasks

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