Monitoring Mental Fatigue through the Analysis of Keyboard and Mouse Interaction Patterns

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

doi:10.1007/978-3-642-40846-5_23

Cited by 49 publications

(48 citation statements)

References 9 publications

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“…In order to test and validate the proposed system and approach, a case study was prepared using detection system models which were previously trained [13,12]. To reach that goal we used two groups of volunteers who used the tool while they were providing feedback of their state of fatigue through USAFSAM Fatigue scale, as well as their mood through the POMS questionnaire.…”

Section: Case Studymentioning

confidence: 99%

Non-intrusive quantification of performance and its relationship to mood

et al. 2016

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The number of jobs that takes place entirely or partially in a computer is nowadays very signi cant. These workplaces, as many others, often o er the key ingredients for the emergence of stress and the performance drop of its long-term e ects: long hours sitting, sustained cognitive e ort, pressure from competitiveness, among others. This has a toll on productivity and work quality, with signi cant costs for both organizations and workers. Moreover, a tired workforce is generally more susceptible to negative feelings and mood, which results in a negative environment. This paper contributes to the current need for the development of non-intrusive methods for monitoring and managing worker performance in real time. We propose a framework that assesses worker performance and a case study in which this approach was validated. We also show the relationship between performance and mood.

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Section: Case Studymentioning

confidence: 99%

Non-intrusive quantification of performance and its relationship to mood

et al. 2016

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“…The trend for data collection regarding sensing on humans is growing and the perspective is for this trend to keep strong, giving the expected growth of IoT (Internet of Things). As described in (Pimenta et al, 2013), by recording the data from the keyboard and mouse movements it is possible to calculate metrics that characterize the user's interaction patterns. The captured records contain fifteen values (represented as doubles) that are a result of applying data redundancy techniques (i.e.…”

Section: Wellness Services Using Real Time Analyticsmentioning

confidence: 99%

“…The way individuals interact with technological devices is influenced by such factors as well and can thus be analyzed to assess them. Some of these effects, analyzed in preliminary studies, show that our performance tends to decrease with the onset of mental fatigue (Pimenta et al, 2013) or that the way we touch or handle a smartphone changes with our level of stress (Carneiro et al, 2012).…”

Section: Performance Assessmentmentioning

confidence: 99%

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Real Time Analytics for Characterizing the Computer User's State

Carneiro

Araújo

Pimenta

et al. 2016

ADCAIJ

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In the last years, the amount of devices that can be connected to a network grew significantly allowing to, among other tasks, collect data about the environment or the people in it in a non-intrusive way. This generated nowadays well-known topics such as Big Data or the Internet of Things. This also opened the door to the development of novel and interesting applications. In this paper we propose a distributed system for acquiring data about the users of technological devices in a non-intrusive way. We describe how this data can be collected and transformed to produce meaningful interaction features, that reveal the state of the individuals. We analyse the requirements of such a system, namely in terms of storage and speed, and describe three prototypes currently being used in three different domains of application.

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“…The way individuals interact with technological devices is influenced by such factors and can thus be assessed to assess them. Some of these effects, analyzed in preliminary studies, show that our performance tends to decrease with the onset of mental fatigue [5] or that the way we touch or handle a smartphone changes with our level of stress [6].…”

Section: Introductionmentioning

confidence: 99%

Quantifying the effects of external factors on individual performance

Carneiro

Nováis

2017

Future Generation Computer Systems

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Monitoring and managing performance in the workplace is nowadays an important aspect, in a time in which methodologies like Agile push individual and team limits further. Current performance monitoring approaches are either intrusive or based on productivity measures and are thus often dreaded by workers. Moreover, these approaches do not take into account the importance and role of the numerous external factors that influence productivity. We present a non-intrusive performance monitoring environment based on behavioral biometrics and real time analytics. It monitors and analyses 15 features extracted from the workers' interaction with the computer and can provide a measure of performance that is completely transparent. This measure is sensitive to external factors such as mental fatigue, stress or emotional valence. We validate this environment by assessing the effects of musical selection on Human-Computer Interaction. Results show a significant improvement on mouse motion when participants listen to the selected auditory stimuli and a negative effect on typing performance, especially with stimuli with positive tension. This work will enable the development of performance monitoring and management environments, with benefits for both organizations and individuals.

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Monitoring Mental Fatigue through the Analysis of Keyboard and Mouse Interaction Patterns

Cited by 49 publications

References 9 publications

Non-intrusive quantification of performance and its relationship to mood

Non-intrusive quantification of performance and its relationship to mood

Real Time Analytics for Characterizing the Computer User's State

Quantifying the effects of external factors on individual performance

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