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• W2058310153 abstract "Interruptions of knowledge workers are common and can cause a high cost if they happen at inopportune moments. With recent advances in psycho-physiological sensors and their link to cognitive and emotional states, we are interested whether such sensors might be used to measure interruptibility of a knowledge worker. In a lab and a field study with a total of twenty software developers, we examined the use of psycho-physiological sensors in a real-world context. The results show that a Naive Bayes classifier based on psycho-physiological features can be used to automatically assess states of a knowledge worker's interruptibility with high accuracy in the lab as well as in the field. Our results demonstrate the potential of these sensors to avoid expensive interruptions in a real-world context. Based on brief interviews, we further discuss the usage of such an interruptibility measure and interruption support for software developers." @default.
• W2058310153 created "2016-06-24" @default.
• W2058310153 creator A5022821596 @default.
• W2058310153 creator A5089828454 @default.
• W2058310153 date "2015-04-18" @default.
• W2058310153 modified "2023-10-12" @default.
• W2058310153 title "Interruptibility of Software Developers and its Prediction Using Psycho-Physiological Sensors" @default.
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• W2058310153 doi "https://doi.org/10.1145/2702123.2702593" @default.
• W2058310153 hasPublicationYear "2015" @default.
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