Interruptibility of Software Developers and its Prediction Using Psycho-Physiological Sensors

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.