Influence of Increasing Task Complexity and Use of Informational Assistance Systems on Mental Workload

Bläsing, Dominic; Bornewasser, Manfred

doi:10.3390/brainsci11010102

Cited by 35 publications

(33 citation statements)

References 65 publications

(92 reference statements)

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“…In industry 4.0 real scenarios, the operator may be subject to task changes, task difficulty, task shared with a robot, and interruptions (for instance, noise) [35,36]. In these cases, understanding the cognitive load types (intrinsic, extraneous, germane) and the relationship with mental workload could be useful for the improvement of the human information processing system, human performance and the effectiveness of the overall system [37,38].…”

Section: Discussionmentioning

confidence: 99%

Cognitive Interaction Analysis in Human–Robot Collaboration Using an Assembly Task

2021

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In human–robot collaborative assembly tasks, it is necessary to properly balance skills to maximize productivity. Human operators can contribute with their abilities in dexterous manipulation, reasoning and problem solving, but a bounded workload (cognitive, physical, and timing) should be assigned for the task. Collaborative robots can provide accurate, quick and precise physical work skills, but they have constrained cognitive interaction capacity and low dexterous ability. In this work, an experimental setup is introduced in the form of a laboratory case study in which the task performance of the human–robot team and the mental workload of the humans are analyzed for an assembly task. We demonstrate that an operator working on a main high-demanding cognitive task can also comply with a secondary task (assembly) mainly developed for a robot asking for some cognitive and dexterous human capacities producing a very low impact on the primary task. In this form, skills are well balanced, and the operator is satisfied with the working conditions.

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Section: Discussionmentioning

confidence: 99%

Cognitive Interaction Analysis in Human–Robot Collaboration Using an Assembly Task

2021

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“…A limitation of the presented solution consists of the fact that it is not suitable for a product with more recipes to assemble or disassemble the product. The impact of task complexity and informational assistance systems is focused on in [19]. Here, the main conclusion after analyzing the mental workload via three physiological indicators (electrocardiography, heart rate variability, eye movement assessment) is that content and structure of the instructions are most important, while the type of assistance system or medium (i.e., paper, tablet, augmented reality device display) is irrelevant (given that the solution used has appropriate usability).…”

Section: Related Workmentioning

confidence: 99%

Robust Assembly Assistance Using Informed Tree Search with Markov Chains

Gellért

Sorostinean

Pirvu

2022

Sensors

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Manual work accounts for one of the largest workgroups in the European manufacturing sector, and improving the training capacity, quality, and speed brings significant competitive benefits to companies. In this context, this paper presents an informed tree search on top of a Markov chain that suggests possible next assembly steps as a key component of an innovative assembly training station for manual operations. The goal of the next step suggestions is to provide support to inexperienced workers or to assist experienced workers by providing choices for the next assembly step in an automated manner without the involvement of a human trainer on site. Data stemming from 179 experiment participants, 111 factory workers, and 68 students, were used to evaluate different prediction methods. From our analysis, Markov chains fail in new scenarios and, therefore, by using an informed tree search to predict the possible next assembly step in such situations, the prediction capability of the hybrid algorithm increases significantly while providing robust solutions to unseen scenarios. The proposed method proved to be the most efficient for next assembly step prediction among all the evaluated predictors and, thus, the most suitable method for an adaptive assembly support system such as for manual operations in industry.

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“…Traditionally, MWL research focuses on longer analysis durations to investigate effects of work on an individuals perceived work load. For example, complete assembly processes or sections lasting several minutes are compared with each other or are used to create progress profiles (Nardolillo et al 2017;Bläsing and Bornewasser 2021). The advantage of such an approach is that for analysis, mostly aggregated and cleaned data can be used, which are less susceptible to measurement errors, spontaneous and non-task related variations, and general noise.…”

Section: Analyzing Mental Workloadmentioning

confidence: 99%

Pattern analysis of physiological data for the assessment of mental workload

Bläsing

2022

Z. Arb. Wiss.

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Measuring mental workload at the workplace using (psycho-) physiological measurement techniques seems desirable but is difficult to implement. Conventional analysis techniques are designed to cover longer measurement durations, neglecting the demands of modern work places: high worker flexibility and constantly fluctuating mental workload. As an alternative analysis approach, measurement (resp. analysis) duration can be shortened and event-based pattern analysis of various physiological parameters can be performed. The effects of such approaches are demonstrated by experimental examples. Furthermore, an event-timestamp independent framework is presented. Focusing on occasionally occurring peaks and longer lasting plateaus in mental workload trajectories, an automatized analysis of workload during work processes becomes possible.Practical relevance: With steadily increasing cognitive demands at work the risk of mental fatigue increases too. Mental workload is not directly observable at the workplace and the objective measurement and interpretation is complicated. Improving the overall assessment and analysis strategies for (physiological) mental workload indicators can benefit the quality of risk assessments of workplaces and processes as well as enable the possibility of demand-orientated control of (informational) assistance systems to prevent mental overload and resulting health constraints.

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Influence of Increasing Task Complexity and Use of Informational Assistance Systems on Mental Workload

Cited by 35 publications

References 65 publications

Cognitive Interaction Analysis in Human–Robot Collaboration Using an Assembly Task

Cognitive Interaction Analysis in Human–Robot Collaboration Using an Assembly Task

Robust Assembly Assistance Using Informed Tree Search with Markov Chains

Pattern analysis of physiological data for the assessment of mental workload

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