Cited by 364Emotion and Mood RecognitionUser Authentication and Security SystemsGaze Tracking and Assistive Technology

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Identifying emotional states using keystroke dynamics

Epp, Clayton, Lippold, Michael, Mandryk, Regan L.·2011·Proceedings of the SIGCHI Conference on Human Factors in Computing Systems

DOI 10.1145/1978942.1979046

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Abstract

The ability to recognize emotions is an important part of building intelligent computers. Emotionally-aware systems would have a rich context from which to make appropriate decisions about how to interact with the user or adapt their system response. There are two main problems with current system approaches for identifying emotions that limit their applicability: they can be invasive and can require costly equipment. Our solution is to determine user emotion by analyzing the rhythm of their typing patterns on a standard keyboard. We conducted a field study where we collected participants' keystrokes and their emotional states via self-reports. From this data, we extracted keystroke features, and created classifiers for 15 emotional states. Our top results include 2-level classifiers for confidence, hesitance, nervousness, relaxation, sadness, and tiredness with accuracies ranging from 77 to 88%. In addition, we show promise for anger and excitement, with accuracies of 84%.

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Authors: Epp, C., Lippold, M., & Mandryk, R. L.
Affiliations: University of Saskatchewan (CA)
ORCID: Mandryk, Regan L.
Venue: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Publisher: ACM
Published: 2011-05-07
Pages: 715-724
DOI: 10.1145/1978942.1979046
OpenAlex: W1978986669
Cited by: 364
References: 37
Citations by year: 2026: 11 · 2025: 16 · 2024: 14 · 2023: 15 · 2022: 46 · 2021: 25 · 2020: 24 · 2019: 36 · 2018: 15 · 2017: 35 · 2016: 21 · 2015: 31 · 2014: 31 · 2013: 20 · 2012: 19
FWCI: 37.63
Type: conference paper
Language: en
Open access: closed
Licence: www.acm.org/publications/policies/copyright_policy#Background (vor)
Locations: CHI Conference on Human Factors in Computing Systems (publishedVersion)
Funders: University of Saskatchewan
Natural Sciences and Engineering Research Council of Canada
Topics: Physical Sciences › Computer Science › Artificial Intelligence › Emotion and Mood Recognition
Physical Sciences › Computer Science › Information Systems › User Authentication and Security Systems
Physical Sciences › Computer Science › Human-Computer Interaction › Gaze Tracking and Assistive Technology
Keywords: emotion recognition, keystroke dynamics, typing patterns, emotional state, emotion-aware systems, user modeling, classification accuracy

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Identifying emotional states using keystroke dynamics

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