A New Method for the Objective Registration of Mental Workload

Radüntz, Thea

doi:10.1007/978-3-319-41691-5_24

Cited by 2 publications

(4 citation statements)

References 6 publications

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“…This hyper-positivist turn in psychology led to the study of what is called neuroergonomics, defined as the study of brain and behaviour at work to align technological and human capabilities with the aim to increase efficiency. The development of non-invasive technologies and their wearability include the potential ‘for monitoring human brain function that can be used to study various aspects of human behaviour in relation to technology and work, including mental workload, visual attention, working memory, motor control, human-automation interaction, and adaptive automation’ (Parasuraman & Rizzo 2007; Radüntz 2016).…”

Section: Beyond Control: Capturing Situated Knowledgementioning

confidence: 99%

Welcome in the machine: Human–machine relations and knowledge capture

Briken

2020

Capital & Class

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This article discusses new technologies in regard to their potential to capture workers’ situated knowledge. Machines are said to substitute but also to contribute to the labour process in collaboration with human skill sets. ‘Industry 4.0’ became the policy-wide shorthand to describe the new quality of real-time interconnectedness and feedback loops, known as cyber-physical systems within industry and engineering sciences. Data flows generated in these systems are used to continuously improve work processes by extracting information down to the very micro-level of neuroergonomics. In this process, workers’ interactions with the system are extracted, fed back and processed for future use and improvement. The article argues that in addition to the potential for extraction of new (bodily) knowledge, shifting skill use and the potential for new forms of control, new technologies contain the potential to extract situated knowledge owned by the worker and crucial for resistance and collective struggles.

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Section: Beyond Control: Capturing Situated Knowledgementioning

confidence: 99%

Welcome in the machine: Human–machine relations and knowledge capture

Briken

2020

Capital & Class

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“…Radüntz [59] found significant differences in accuracy rates among all fatigue levels. The accuracy of controlling vehicles was found to have a sufficient correlation with fatigue level.…”

Section: Physiological Datamentioning

confidence: 91%

“…However, some studies found that using HRV as a fatigue indicator was somewhat unreliable [58]. In addition to heart rate variables, other well-known physiological measures exist, such as electrodermal response (EDR), respiration rate (RR) and blood pressure (BP) [58,59].…”

Section: Fatigue Measurementmentioning

confidence: 99%

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Data-driven, context-aware human fatigue management in traffic control centers

Fan¹

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Traffic control operators (TCOs) are at risk of human fatigue due to their heavy workload, their monotonous work, and the long duration of their shift work. Human fatigue can result in inefficiency, impaired alertness, and even a high probability of human error, and it has been identified as the primary cause of many traffic accidents. Thus, much research has focused on the management of human fatigue in transportation. However, strategies for managing human fatigue in transportation mainly focus on a single aspect of human fatigue, such as mental fatigue or physical fatigue. In addition, the existing strategies primarily center on prescriptive hours-of-work regulations. However, such regulations lack flexibility and fail to consider factors such as individual differences and the dynamic working conditions involved in traffic control. To CF18: Too many unnecessary alarms CF19: Ambiguous alarms CF20: Time pressure in providing information Assign user event (UE)CF16: Inefficient communication due to language barrier CF21: Too many steps to make assignments CF22: Lack of experience CF23: Difficulties in clicking vessels using the mouseIn addition to these task-related factors, 24 factors were collected by SCMbased analysis, as listed in Table 3.3.

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A New Method for the Objective Registration of Mental Workload

Cited by 2 publications

References 6 publications

Welcome in the machine: Human–machine relations and knowledge capture

Welcome in the machine: Human–machine relations and knowledge capture

Data-driven, context-aware human fatigue management in traffic control centers

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