Automated stress detection using keystroke and linguistic features: An exploratory study

Vizer, Lisa M.; Zhou, Lina; Sears, Andrew

doi:10.1016/j.ijhcs.2009.07.005

Cited by 184 publications

(123 citation statements)

References 34 publications

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“…These are: backspace, right arrow, left arrow, enter. It confirmed some observations made in other studies [11].…”

Section: A Resultssupporting

confidence: 93%

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Towards detecting programmers’ stress on the basis of keystroke dynamics

Kołakowska¹

2016

Annals of Computer Science and Information Systems

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Abstract-The article describes the idea of detecting stress among programmers on the basis of keystroke dynamics. An experiment with a group of students of artificial intelligence classes was performed. Two samples of keystroke data were recorded for each case, the first while programming without stress, the second under time pressure. A number of timing and frequency parameters were calculated for each sample. Then statistical analysis was performed to evaluate the significance of keystroke parameters changes. It turned out that some of the defined features might be indicators of being stressed.

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“…These are: backspace, right arrow, left arrow, enter. It confirmed some observations made in other studies [11].…”

Section: A Resultssupporting

confidence: 93%

“…In [11] for example an experiment on recognizing stress on the basis of keystroke and linguistic features has been presented. The stress was induced by giving some stressful tasks to the participants.…”

Section: Related Workmentioning

confidence: 99%

Towards detecting programmers’ stress on the basis of keystroke dynamics

Kołakowska¹

2016

Annals of Computer Science and Information Systems

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“…Data can be sampled from time to time, but may not be suitable for detecting the subtle changes which could indicate an early stage of a major problem [296] neither realistic to carry out a continuous monitoring of the disease progress [297]. Actually, they are only measured when the affected themselves or the people around them realize or suspect about the severity of the situation, and this is too late in the vast majority of the cases.…”

Section: Critical Reviewmentioning

confidence: 99%

“…Regarding CSF measurements, they are intrusive, costly and slow methods of analysis [300]. Furthermore, all of these current tests, are "usually administered in a physician's office or a rehabilitation facility, causing inconvenience for the patient, using valuable healthcare resources, making frequent monitoring unrealistic" [296] and therefore, precluding an early diagnosis. As said before (see Section 1.3), early detection of AD would bring many benefits, in terms of treatments' effectiveness and accuracy of diagnosis.…”

Section: Critical Reviewmentioning

confidence: 99%

On the early diagnosis of Alzheimer's Disease from multimodal signals: A survey

Alberdi

Aztiria

Basarab

2016

Artificial Intelligence in Medicine

164

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“…Other authors have also looked at Behavioral Biometrics for similar purposes. Vizer et al [20] are able to classify cognitive and physical stress with accuracy rates comparable to those currently obtained using affective computing methods, using keystroke and linguistic features of spontaneously generated text. A case-based approach relying on Behavioural Biometrics is used by [21] to determine a user's stress level.…”

Section: Related Workmentioning

confidence: 86%

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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Automated stress detection using keystroke and linguistic features: An exploratory study

Cited by 184 publications

References 34 publications

Towards detecting programmers’ stress on the basis of keystroke dynamics

Towards detecting programmers’ stress on the basis of keystroke dynamics

On the early diagnosis of Alzheimer's Disease from multimodal signals: A survey

Quantifying the effects of external factors on individual performance

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