Towards detecting programmers’ stress on the basis of keystroke dynamics

Kołakowska, Agata

doi:10.15439/2016f263

Cited by 27 publications

(25 citation statements)

References 15 publications

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“…Previous studies have collected behavioral data from computer, keystroke, and mouse dynamics such as interaction time, typing pressure, and mouse clicking, but mostly under laboratory conditions [21][22][23][24][25]. The majority of real-life stress-detection studies have been based on behavioral data gathered from smartphones [26][27][28][29][30][31][32].…”

Section: Unobtrusive Stress Detectionmentioning

confidence: 99%

Unobtrusive Continuous Stress Detection in Knowledge Work—Statistical Analysis on User Acceptance

et al. 2021

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Modern knowledge work is highly intense and demanding, exposing workers to long-term psychosocial stress. In order to address the problem, stress detection technologies have been developed, enabling the continuous assessment of personal stress based on multimodal sensor data. However, stakeholders lack insights into how employees perceive different monitoring technologies and whether they are willing to share stress-indicative data in order to sustain well-being at the individual, team, and organizational levels in the knowledge work context. To fill this research gap, we developed a theoretical model for knowledge workers’ interest in sharing their stress-indicative data collected with unobtrusive sensors and examined it empirically using structural equation modeling (SEM) with a survey of 181 European knowledge workers. The results did not show statistically significant privacy concerns regarding environmental sensors such as air quality, sound level, and motion sensors. On the other hand, concerns about more privacy-sensitive methods such as tracking personal device usage patterns did not prevent user acceptance nor intent to share data. Overall, knowledge workers were highly interested in employing stress monitoring technologies to measure their stress levels and receive information about their personal well-being. The results validate the willingness to accept the unobtrusive, continuous stress detection in the context of knowledge work.

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Section: Unobtrusive Stress Detectionmentioning

confidence: 99%

Unobtrusive Continuous Stress Detection in Knowledge Work—Statistical Analysis on User Acceptance

et al. 2021

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“…It can only be used in a multimodal approach. On the other hand, extended pupil movement pattern analysis, combined with keystroke dynamics or mouse movement [30], can reveal interesting results.…”

Section: Discussionmentioning

confidence: 99%

“…It is completely non-intrusive and does not require any additional hardware [29]. There have already been attempts to use this method to monitor software developers [30].…”

Section: Related Workmentioning

confidence: 99%

“…On Computer 2, Morae Recorder software was installed, which recorded the participant's desktop image, mouse movement, and fixation from the eye tracker. There was also a data acquisition program for keystroke analysis available on the same computer [30]. Computer 3 was used to collect all other data useful for the recognition of emotions, including recordings from the video camera and the microphone located in front of the participant.…”

Section: • Computer Keyboard and Mouse Movementsmentioning

confidence: 99%

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Applicability of Emotion Recognition and Induction Methods to Study the Behavior of Programmers

Wróbel¹

2018

Applied Sciences

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Recent studies in the field of software engineering have shown that positive emotions can increase and negative emotions decrease the productivity of programmers. In the field of affective computing, many methods and tools to recognize the emotions of computer users were proposed. However, it has not been verified yet which of them can be used to monitor the emotional states of software developers. The paper describes a study carried out on a group of 35 participants to determine which of these methods can be used during programming. During the study, data from multiple sensors that are commonly used in methods of emotional recognition were collected. The participants were extensively questioned about the sensors' invasiveness during programming. This allowed us to determine which of them are applicable in the work of programmers. In addition, it was verified which methods are suitable for use in the work environment and which are only suitable in the laboratory. Moreover, three methods for inducing negative emotions have been proposed, and their effectiveness has been verified.

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“…A careful analysis of the existing studies ( [7]) evidences that attention can be detected via various mechanisms including keystroke dynamics [8] and pupillography [9]; however EEG provide in general the most accurate results among the approaches that are feasible to use [10][11][12][13][14][15].…”

Section: Background and Related Workmentioning

confidence: 99%

Attention tracking for developers

Amirova

2020

Proceedings of the 28th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Softw

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Towards detecting programmers’ stress on the basis of keystroke dynamics

Cited by 27 publications

References 15 publications

Unobtrusive Continuous Stress Detection in Knowledge Work—Statistical Analysis on User Acceptance

Unobtrusive Continuous Stress Detection in Knowledge Work—Statistical Analysis on User Acceptance

Applicability of Emotion Recognition and Induction Methods to Study the Behavior of Programmers

Attention tracking for developers

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