The Other Kind of Machine Learning: Modeling Worker State for Optimal Training of Novices in Complex Industrial Processes

Thomay, Christian; Gollan, Benedikt; Haslgrübler, Michael; Ferscha, Alois; Heftberger, Josef

doi:10.1109/iceta.2018.8572151

Cited by 3 publications

(3 citation statements)

References 17 publications

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“…Two years later, Tortora et al ( 2021 ) highlighted that even though experience and training have been identified as the most critical human factors for performance, research still had not considered them. However, even in the studies developed by Thomay et al ( 2018 ), Madonna et al ( 2019 ), Digiesi et al ( 2020 ), Cimini et al ( 2020 ), and Van Acker et al ( 2020 ), the cognitive workload is included but focused mainly on performance, evaluated through physical parameters and task sequences, with the aim of evaluating the so-called “human errors.” In the case of the latter study, the use of these measures is argued as “relevant for situations in which the consequences of hesitative behavior can be detrimental” (Van Acker et al, 2020 , p. 35). But in real working conditions, and in industrial production settings in which productivity continues to be the main focus, will there be the guarantee that work is interrupted when the cognitive workload affects workers' health?…”

Section: Discussionmentioning

confidence: 99%

“…I4.0 technologies were described through case studies on specific components, which included both implementation and trial studies, as well as their expected outcomes and limits. Richert et al, 2016b;Kolbeinsson et al, 2017;Langfinger et al, 2017;Thomay et al, 2018;Ivaschenko et al, 2019;Minnetti et al, 2019;Pavel et al, 2019;Baldissone et al, 2020;Digiesi et al, 2020;Fruggiero et al, 2020;Gualtieri et al, 2020;Kadir and Broberg, 2020;Santo et al, 2020;Shi et al, 2020;Van Acker et al, 2020;Agnusdei et al, 2021. RQ2: How is the play in these contexts. Kolbeinsson et al, 2017;Marrella and Mecella, 2017;Schloegl et al, 2017;Badri et al, 2018;Mark et al, 2019;Thun et al, 2019;Udayangani et al, 2019;Ahmad et al, 2020;Blštáková et al, 2020;Broday, 2020;Cimini et al, 2020;Gajšek et al, 2020;Gazzaneo et al, 2020;Gualtieri et al, 2020;Hoyer et al, 2020;Kaasinen et al, 2020;Broberg, 2020, 2021;Longo et al, 2020;Saabye et al, 2020;Sony and Naik, 2020;Chistyakova et al, 2021;Di Carlo et al, 2021;…”

Section: Discussionmentioning

confidence: 99%

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Exploring the status of the human operator in Industry 4.0: A systematic review

2022

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Industry 4.0 (I4.0) promises to transform jobs and working conditions through the implementation of unprecedented human-machine interaction modes. As the operator working in these new settings, known as the Operator 4.0, is a relatively recent concept, and although technological developments are expected to support workers and require higher labor skills, the risks and health impacts resulting from these changes remain underexplored. This systematic review aims to (i) systematize literature findings on how workers are perceived and participate in I4.0 work systems; (ii) identify the main technological changes driven by I4.0; and (iii) instigate discussion regarding the impacts these changes may have for workers and the sustainability of work systems. Following a systematic review approach using the PRISMA protocol, the articles were organized into two main analysis axes: the technical changes brought about by I4.0, and the representation of the human worker within these new work settings. The findings reveal that a techno-centered approach still seems to be dominant in guiding the implementation of I4.0 models; secondly, as a consequence, the social dimensions of work tend to remain as residual issues, overshadowed by the promises related with technology (e.g., productivity, efficiency); finally, the representation of the Operator 4.0 remains blurry, as he/she is perceived as gender neutral, skillful, and perfectly fit for work, assuring the functioning (and compensating for the limits) of these systems. While I4.0 promises safer and more productive workplaces, issues related to employment conditions, emerging risks and health impacts become more prominent when analyzed from an activity-centered perspective. In terms of future research, a more heuristic analysis could be achieved through a participatory and work-centered approach and following a gender perspective. This way, visibility could be conferred to another side of I4.0, thus guaranteeing conditions for the sustainable development of these work situations.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Exploring the status of the human operator in Industry 4.0: A systematic review

2022

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“…As a final description of this section, the work that was carried out by Thomay et al [44], who discussed the concept of e-learning in a training station, intended to teach new or novice workers the steps to follow to assemble a ski without the need for frequent human supervision. The training technique developed allows, based on the needs and behavior of the user, the delivery of the necessary information and instructions in order to complete their task.…”

Section: Industrial Trainingmentioning

confidence: 99%

A Scoping Review on Virtual Reality-Based Industrial Training

et al. 2020

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The fourth industrial revolution has forced most companies to technologically evolve, applying new digital tools, so that their workers can have the necessary skills to face changing work environments. This article presents a scoping review of the literature on virtual reality-based training systems. The methodology consisted of four steps, which pose research questions, document search, paper selection, and data extraction. From a total of 350 peer-reviewed database articles, such as SpringerLink, IEEEXplore, MDPI, Scopus, and ACM, 44 were eventually chosen, mostly using the virtual reality haptic glasses and controls from Oculus Rift and HTC VIVE. It was concluded that, among the advantages of using this digital tool in the industry, is the commitment, speed, measurability, preservation of the integrity of the workers, customization, and cost reduction. Even though several research gaps were found, virtual reality is presented as a present and future alternative for the efficient training of human resources in the industrial field.

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A Review on Enabling Technologies of Industrial Virtual Training Systems

Singhaphandu,

Pannakkong

2024

International Journal of Knowledge and Systems Science

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Industrial training increasingly uses computer technologies to boost efficiency, effectiveness, and safety. Virtual reality (VR), augmented reality (AR), and e-learning platforms have transformed traditional training methods. This review examines these technologies and highlights their applications in various sectors. The review addresses adoption challenges, including high costs, technical complexity, user acceptance, learning curves, simulation realism, and health concerns. It proposes strategies to mitigate these issues, such as cost-effective solutions, improved usability, enhanced realism, and robust assessment mechanisms. By analyzing multiple studies, the paper provides insights into the current state and future directions of technology-enabled industrial training, emphasizing ongoing innovation and adaptation to maximize these advanced tools' potential.

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The Other Kind of Machine Learning: Modeling Worker State for Optimal Training of Novices in Complex Industrial Processes

Cited by 3 publications

References 17 publications

Exploring the status of the human operator in Industry 4.0: A systematic review

Exploring the status of the human operator in Industry 4.0: A systematic review

A Scoping Review on Virtual Reality-Based Industrial Training

A Review on Enabling Technologies of Industrial Virtual Training Systems

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