Fatigue Stressors in Simulated Long-Duration Flight. Effects on Performance, Information Processing, Subjective Fatigue, and Physiological Cost

Perelli, Layne P.

doi:10.21236/ada105484

Cited by 15 publications

(15 citation statements)

References 49 publications

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“…While additional research is required to clarify the relationship (8), it is hypothesized that scores of 4-7 may indicate performance impairment and that scores of 3 or less very likely indicate degraded performance on certain complex demanding tasks.…”

Section: Methodsmentioning

confidence: 99%

Fatigue in double-crew aerial-refueled transport missions

Storm¹,

Dowd²,

Noga³

et al. 1981

PsycEXTRA Dataset

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Section: Methodsmentioning

confidence: 99%

Fatigue in double-crew aerial-refueled transport missions

Storm¹,

Dowd²,

Noga³

et al. 1981

PsycEXTRA Dataset

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“…Simultaneously with the acquisition of the behavioral features, employees answered a questionnaire about mental fatigue on a hourly basis (USAFSAM Fatigue Scale [13]). This was implemented with the aim of studying, in parallel, the daily evolution of mental fatigue given the well-known relationship between this indicator and performance [14].…”

Section: Architecturementioning

confidence: 99%

A multi-modal architecture for non-intrusive analysis of performance in the workplace

et al. 2017

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Human performance, in all its di erent dimensions, is a very complex and interesting topic. In this paper we focus on performance in the workplace which, asides from complex is often controversial. While organizations and generally competitive working conditions push workers into increasing performance demands, this does not necessarily correlates positively to productivity. Moreover, existing performance monitoring approaches (electronic or not) are often dreaded by workers since they either threat their privacy or are based on productivity measures, with speci c side e ects. We present a new approach for the problem of performance monitoring that is not based on productivity measures but on the workers' movements while sitting and on the performance of their interaction with the machine. We show that these features correlate with mental fatigue and provide a distributed architecture for the non-intrusive and transparent collection of this data. The easiness in deploying this architecture, its non-intrusive nature, the potential advantages for better human resources management and the fact that it is not based on productivity measures will, in our belief, increase the willingness of both organizations and workers to implement this kind of performance management initiatives.

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“…Performance decrement due to fatigue may be considered as the total quantity and/or quality of work performed as a function of time on task [6]. Cognitive performance is often measured as the combination of the accuracy per response time of an individual when carrying out a task.…”

Section: Performance Measuresmentioning

confidence: 99%

“…Thus, the concept of fatigue is very broad and subjective. It can be seen as a combination of symptoms, including impaired performance (loss of attention, slowed reaction and response times, impaired decision making, and poor performance on tasks that usually reflect good performance), and subjective feelings of sleepiness and tiredness [5,6]. It can also be divided in two main types [7]: mental fatigue (when it is our cognitive abilities that are decreased) and physical fatigue (when we become physically impaired due to an excess of a specific physical activity).…”

Section: Introductionmentioning

confidence: 99%

A neural network to classify fatigue from human–computer interaction

et al. 2016

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a b s t r a c tFatigue, especially in its mental form, is one of the most worrying health problems nowadays. It affects not only health but also motivation, emotions and feelings and has an impact both at the individual and organizational level. Fatigue monitoring and management assumes thus, in this century, an increased importance, that should be promoted by private organizations and governments alike. While traditional approaches are mostly based on questionnaires, in this paper we present an alternative one that relies on the observation of the individual's interaction with the computer. We show that this interaction changes with the onset of fatigue and that these changes are significant enough to support the training of a neural network that can classify mental fatigue in real time. The main outcome of this work is the development of non-invasive systems for the continuous classification of mental fatigue that can support effective and efficient fatigue management initiatives, especially in the context of desk jobs.

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Fatigue Stressors in Simulated Long-Duration Flight. Effects on Performance, Information Processing, Subjective Fatigue, and Physiological Cost

Cited by 15 publications

References 49 publications

Fatigue in double-crew aerial-refueled transport missions

Fatigue in double-crew aerial-refueled transport missions

A multi-modal architecture for non-intrusive analysis of performance in the workplace

A neural network to classify fatigue from human–computer interaction

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