Detection of Distraction and Fatigue in Groups through the Analysis of Interaction Patterns with Computers

Pimenta, Alexandre Dias; Carneiro, Davide; Nováis, Paulo; Neves, José

doi:10.1007/978-3-319-10422-5_5

Cited by 20 publications

(17 citation statements)

References 8 publications

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“…From the mouse we extract features such as velocity, acceleration, distance between clicks, duration of the click or excess of distance traveled, just to name a few. Once again, we consistently nd decreases in performance on these features as the workday progresses [12].…”

Section: Architecturesupporting

confidence: 53%

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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Section: Architecturesupporting

confidence: 53%

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

et al. 2017

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“…text typing and web browsing) from the interaction patterns, identifying workload and quantifying the level of task attention [47]. This kind of information, which to some extent describes the user's context, will enable the development of more accurate classifiers.…”

Section: Discussionmentioning

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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“…The CLA joins the strongest features of both referred projects and includes intelligent social computing (Sheth et al, 2013;Wang et al, 2007) and the human behaviour emulation (Gomes et al, 2014;Pimenta et al, 2015;Carneiro et al, 2013). The CLA joins the strongest features of both referred projects and includes intelligent social computing (Sheth et al, 2013;Wang et al, 2007) and the human behaviour emulation (Gomes et al, 2014;Pimenta et al, 2015;Carneiro et al, 2013).…”

Section: Future Path and Conclusionmentioning

confidence: 98%

Advances and trends for the development of ambient‐assisted living platforms

2016

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Ambient Assisted Living (AAL) and Ambient Intelligence (AmI) try to achieve a future where technology surrounds the users and helps them in their daily lives. In this sense, the urgent need of solutions to cover the rapid increase of the elderly population with chronic diseases led to the increase of projects related with AAL and AmI. During the latest years, several projects have been proposed to tackle different medical problems, some building devices and others services. This paper presents iGenda and its evolution, the UserAccess, with the main objective of developing an AAL platform. It features an analysis of the latest developments and points future directions for the work. These projects display the importance of the interoperability of the platforms, demonstrating a case study for AAL development.

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Detection of Distraction and Fatigue in Groups through the Analysis of Interaction Patterns with Computers

Cited by 20 publications

References 8 publications

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

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

A neural network to classify fatigue from human–computer interaction

Advances and trends for the development of ambient‐assisted living platforms

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