Identifying emotional states using keystroke dynamics

By Clayton Epp, Michael Lippold, Regan L. Mandryk. International Conference on Human Factors in Computing Systems, 2011

379 citations recorded by the source corpus.

Subfields: Cognitive Neuroscience, Experimental and Cognitive Psychology, Information Systems

View Paper: https://www.semanticscholar.org/paper/275445087a85bf6d3b0f101a60b0be7ab4ed520f

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Cognitive Neuroscience

1. Mi Zhang
2. Stephen M. Schueller
3. David C. Mohr

In Experimental and Cognitive Psychology

1. Adrian Basarab
2. Mi Zhang
3. Asier Aztiria
4. Ane Alberdi
5. Stephen M. Schueller
6. David C. Mohr

In Information Systems

1. Stephen M. Schueller
2. David C. Mohr

Citations in the corpus

• Personal Sensing: Understanding Mental Health Using Ubiquitous Sensors and Machine Learning (2017, background): “One study tracking everyday computer use for 1 month among 12 participants found promising accuracy (70–88%) for self-reported emotion (Epp et al. 2011).”
• Towards an automatic early stress recognition system for office environments based on multimodal measurements: A review (2016, background): “Keystroke dynamics is the study of the unique characteristics that are present in an individual’s typing rhythm when using a keyboard or keypad [91].”
• Biometric Authentication and Identification using Keystroke Dynamics: A Survey (2012, neutral): “[54].”