Behaviour and Physiology Publications
Behaviour and Physiology Research
Publications focused on understanding human behaviour through physiological responses, eye tracking, scanpath analysis, and behavioural sensing.
28 publications | ← Back to all publications
2025
Alsayahani, H. et al. (2025). “The Effect of Nudging Techniques on the Customisation and Usability of Visual Analytics Dashboards”. Proceedings of the 33rd ACM Conference on User Modeling, Adaptation and Personalization, pp. 204–213. DOI: 10.1145/3699682.3728352.
Alsayahani, H. et al. (2025). “The Effects of Customisation on the Usability of Visual Analytics Dashboards: the Good, the Bad, and the Ugly”. Proceedings of the 30th International Conference on Intelligent User Interfaces, pp. 1426–1439. DOI: 10.1145/3708359.3712120.
2023
Dunne, R., Matthews, O., Hernandez, V. (2023). “Computational methods for predicting human behaviour in smart environments”. Journal of Ambient Intelligence and Smart Environments, 15(2), 179–205. DOI: 10.3233/AIS-210384.
2022
Dunne, R., Morris, T., Harper, S. (2022). “A semantic blocks model for human activity prediction in smart environments using time-windowed contextual data”. Journal of Reliable Intelligent Environments. DOI: 10.1007/s40860-022-00194-1.
Ibrahim, A., Clinch, S., Harper, S. (2022). “Extracting behavioural features from smartphone notifications”. Behaviour. DOI: 10.1080/0144929X.2022.2145996.
2021
Ibrahim, A. et al. (2021). “From GPS to semantic data: how and why—a framework for enriching smartphone trajectories”. Computing. DOI: 10.1007/s00607-021-00993-z.
Ibrahim, A. et al. (2021). “Digital Phenotypes for Understanding Individuals’ Compliance With COVID-19 Policies and Personalized Nudges: Longitudinal Observational Study”. JMIR Formative Research, 5(5). DOI: 10.2196/23461.
Dunne, R., Morris, T., Harper, S. (2021). “A Survey of Ambient Intelligence”. ACM Computing Surveys, 54(4), 1–27. DOI: 10.1145/3447242.
Ibrahim, A., Clinch, S., Harper, S. (2021). “Recognising Intrinsic Motivation using Smartphone Trajectories”. International Journal of Human-Computer Studies, 153. DOI: 10.1016/j.ijhcs.2021.102650.
2020
Dunne, R., Morris, T., Harper, S. (2020). “High accuracy classification of COVID-19 coughs using Mel-frequency cepstral coefficients and a Convolutional Neural Network with a use case for smart home devices”. DOI: 10.21203/rs.3.rs-63796/v1.
Ibrahim, A. et al. (2020). “Digital Phenotypes for Understanding Individuals’ Compliance With COVID-19 Policies and Personalized Nudges: Longitudinal Observational Study (Preprint)”. DOI: 10.2196/preprints.23461.
Matthews, O. et al. (2020). “Unobtrusive Arousal Detection on the Web Using Pupillary Response”. International Journal of Human-Computer Studies, 136. DOI: 10.1016/j.ijhcs.2019.09.003.
2019
Matthews, O. et al. (2019). “Combining Trending Scan Paths with Arousal to Model Visual Behaviour on the Web: A Case Study of Neurotypical People vs People with Autism”. ACM Conference On User Modelling, Adaptation And Personalization. DOI: 10.1145/3320435.3320446.
2018
Eraslan, S. et al. (2018). “Web Users with Autism: Eye Tracking Evidence for Differences”. Behaviour and Information Technology. DOI: 10.1080/0144929x.2018.1551933.
Matthews, O., Vigo, M., Harper, S. (2018). “Sensing Arousal and Focal Attention During Visual Interaction”. ICMI 2018 - Proceedings of the 2018 International Conference on Multimodal Interaction, pp. 263–267. DOI: 10.1145/3242969.3243005.
Davies, A. et al. (2018). “Using simultaneous scanpath visualization to investigate the relationship between accuracy and eye movement during medical image interpretation”. The Journal of Eye Movement Research, 10(5). DOI: 10.16910/jemr.10.5.11.
2017
Eraslan, S., Yesilada, Y., Harper, S. (2017). “Less Users More Confidence: How AOIs Don’t Affect Scanpath Trend Analysis”. The Journal of Eye Movement Research, 10(4). DOI: 10.16910/jemr.10.4.6.
Eraslan, S., Yesilada, Y., Harper, S. (2017). “Engineering web-based interactive systems: trend analysis in eye tracking scanpaths with a tolerance”. Proceedings of the ACM SIGCHI Symposium on Engineering Interactive Computing Systems, pp. 3–8.
2016
Davies, A. et al. (2016). “The Visualisation of Eye-tracking Scanpaths: What can they tell us about how Clinicians View Electrocardiograms?”.
Davies, A. et al. (2016). “Computational Methods for Analysis of Visual Behavior using Eye-tracking”. Measuring Behavior 2016.
Eraslan, S., Yeliz, Y., Harper, S. (2016). “Eye tracking scanpath analysis on web pages: how many users?”. Proceedings of the Ninth Biennial ACM Symposium on Eye Tracking Research & Applications, pp. 103–110. DOI: 10.1145/2857491.2857519.
Eraslan, S., Yesilada, Y., Harper, S. (2016). “Scanpath Trend Analysis on Web Pages: Clustering Eye Tracking Scanpaths”. ACM Transactions on the Web. DOI: 10.1145/2970818.
Eraslan, S., Yesilada, Y., Harper, S. (2016). “Trends in Eye Tracking Scanpaths: Segmentation Effect?”, pp. 15–25. DOI: 10.1145/2914586.2914591.
Eraslan, S. et al. (2016). “What is Trending in Eye Tracking Scanpaths on Web Pages?”, pp. 360–362.
2015
Eraslan, S., Yesilada, Y., Harper, S. (2015). “Eye Tracking Scanpath Analysis Techniques on Web Pages: A Survey, Evaluation and Comparison”. The Journal of Eye Movement Research, 9(1), 1–19. DOI: 10.16910/jemr.9.1.2.
2014
Apaolaza, A. et al. (2014). “Understanding the division of attention between TV and companion content: experiment 2, without eye-tracking”.
Eraslan, S., Yesilada, Y., Harper, S. (2014). “Identifying patterns in eyetracking scanpaths in terms of visual elements of web pages”.
2010
Brown, A., Jay, C., Harper, S. (2010). “Using qualitative eye-tracking data to inform audio presentation of dynamic Web content”. New Review of Hypermedia and Multimedia, 16(3), 281–301. DOI: 10.1080/13614568.2010.542253.
Research Themes
Eye Tracking & Visual Attention
Using eye tracking technology to understand visual attention patterns, scanpath analysis, and gaze behaviour during web interaction.
Physiological Sensing
Detecting arousal, cognitive load, and emotional states through physiological signals such as pupillary response.
Behavioural Analytics
Extracting meaningful behavioural patterns from smartphone sensors, notifications, and user interactions to understand habits and motivations.
Ambient Intelligence
Predicting human activity and behaviour in smart environments using contextual data and machine learning.