A review of emotion recognition methods based on keystroke dynamics and mouse movements
Abstract
The paper describes the approach based on using standard input devices, such as keyboard and mouse, as sources of data for the recognition of users' emotional states. A number of systems applying this idea have been presented focusing on three categories of research problems, i.e. collecting and labeling training data, extracting features and training classifiers of emotions. Moreover the advantages and examples of combining standard input devices with other sources of information on human emotions have been also described. Eventually some conclusions from the review have been drawn.
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Keystroke Dynamics can be used to determine the authors' emotional state.
“Besides its natural use for authentication, KD can also be used to determine the authors' emotional state [102][103][104][105]”
From Online grooming detection: A comprehensive survey of child exploitation in chat logs (2023) · cites this paper as [103]
Cites
Measuring emotion: The self-assessment manikin and the semantic differential
Bradley, M. M., & Lang, P. J. (1994) · Journal of Behavior Therapy and Experimental Psychiatry · doi:10.1016/0005-7916(94)90063-9 · Cited by 9,538 · see citing works
Affect Detection: An Interdisciplinary Review of Models, Methods, and Their Applications
Calvo, R. A., & D'Mello, S. (2010) · IEEE Transactions on Affective Computing · doi:10.1109/t-affc.2010.1 · Cited by 1,893 · see citing works
Identifying emotional states using keystroke dynamics
Epp, C., Lippold, M., & Mandryk, R. L. (2011) · Proceedings of the SIGCHI Conference on Human Factors in Computing Systems · doi:10.1145/1978942.1979046 · Cited by 364 · see citing works
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Zulueta, J., Piscitello, A., Rasic, M., Easter, R., Babu, P., Langenecker, S. A., McInnis, M., Ajilore, O., Nelson, P. C., Ryan, K., et al. (2018) · Journal of Medical Internet Research · doi:10.2196/jmir.9775 · Cited by 233 · see citing works
Online grooming detection: A comprehensive survey of child exploitation in chat logs
Borj, P. R., Raja, K., & Bours, P. (2023) · Knowledge-Based Systems · doi:10.1016/j.knosys.2022.110039 · Cited by 38 · see citing works
Vizer, L. M., & Sears, A. (2017) · International Journal of Human-Computer Studies · doi:10.1016/j.ijhcs.2017.03.001 · Cited by 12 · see citing works
- Authors
- Kolakowska, A.
- Venue
- 2013 6th International Conference on Human System Interactions (HSI)
- Publisher
- IEEE
- Published
- 2013-06
- Pages
- 548-555
- OpenAlex
- W2111866722
- Cited by
- 130
- References
- 3
- Citations by year
- 2026: 2 · 2025: 8 · 2024: 6 · 2023: 6 · 2022: 10 · 2021: 11 · 2020: 9 · 2019: 9 · 2018: 17 · 2017: 13 · 2016: 13 · 2015: 15 · 2014: 9 · 2013: 2
- FWCI
- 17.69
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- Conference on Human System Interactions (publishedVersion)
- Topics
- Social Sciences › Psychology › Experimental and Cognitive Psychology › 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, mouse movement, physiological signals, training data collection, feature extraction, multimodal emotion recognition, user emotions
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