References
- Agarwal, A., & Meyer, A. (2009). Beyond usability: Evaluating emotional response as an integral part of the user experience. CHI ’09 Extended abstracts on Human Factors in Computing Systems (pp. 2919–2930). https://doi.org/https://doi.org/10.1145/1520340.1520420
- Agrafioti, F., Hatzinakos, D., & Anderson, A. K. (2011). ECG pattern analysis for emotion detection. IEEE Transactions on Affective Computing, 3(1), 102–115. https://doi.org/https://doi.org/10.1109/T-AFFC.2011.28
- Alberdi, A., Aztiria, A., & Basarab, A. (2016). Towards an automatic early stress recognition system for office environments based on multimodal measurements: A review. Journal of Biomedical Informatics, 59(C), 49–75. https://doi.org/https://doi.org/10.1016/j.jbi.2015.11.007
- Albert, W., & Tullis, T. (2013). Measuring the user experience: Collecting, analyzing, and presenting usability metrics. Newnes.
- Baltaci, S., & Gokcay, D. (2016). Stress detection in human–computer interaction: Fusion of pupil dilation and facial temperature features. International Journal of Human–Computer Interaction, 32(12), 956–966. https://doi.org/https://doi.org/10.1080/10447318.2016.1220069
- Baum, A. (1990). Stress, intrusive imagery, and chronic distress. Health Psychology: Official Journal of the Division of Health Psychology, American Psychological Association, 9(6), 653–675. https://doi.org/https://doi.org/10.1037/0278-6133.9.6.653
- Berkowitz, S. (2013). Using qualitative and mixed-method approaches. In Needs assessment (pp. 69–86). Taylor & Francis.
- Blythe, M. A., Overbeeke, K., Monk, A. F., & Wright, P. C. (Eds.). (2004). Funology: From usability to enjoyment. Springer Netherlands.
- Boucsein, W. (2012). Electrodermal activity (2nd ed.). Springer US.
- Brooke, J. (1996). SUS-A quick and dirty usability scale. Usability Evaluation in Industry, 189(194), 4–7.
- Bruun, A. (2018). It’s not complicated: A study of non-specialists analyzing GSR sensor data to detect UX related events. Proceedings of the 10th Nordic Conference on Human-Computer Interaction (pp. 170–183). https://doi.org/https://doi.org/10.1145/3240167.3240183
- Bruun, A., Law, E. L.-C., Heintz, M., & Alkly, L. H. A. (2016). Understanding the relationship between frustration and the severity of usability problems: What can psychophysiological data (not) tell us? Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems (pp. 3975–3987). https://doi.org/https://doi.org/10.1145/2858036.2858511
- Calhoun, B. H., Lach, J., Stankovic, J., Wentzloff, D. D., Whitehouse, K., Barth, A. T., Brown, J. K., Li, Q., Oh, S., Roberts, N. E., & Zhang, Y. (2012). Body sensor networks: A holistic approach from silicon to users. Proceedings of the IEEE, 100(1), 91–106. https://doi.org/https://doi.org/10.1109/JPROC.2011.2161240
- Chow, C., & Gedeon, T. (2017). Evaluating crowdsourced relevance assessments using self-reported traits and task speed. Proceedings of the 29th Australian Conference on Computer-Human Interaction (pp. 407–411). https://doi.org/https://doi.org/10.1145/3152771.3156146
- Cohen, J. (1960). A coefficient of agreement for nominal scales. Educational and Psychological Measurement, 20(1), 37–46. https://doi.org/https://doi.org/10.1177/001316446002000104
- Cohen, J. (1992). A power primer. Psychological Bulletin, 112(1), 155–159. https://doi.org/https://doi.org/10.1037/0033-2909.112.1.155
- Courtemanche, F., Léger, P.-M., Dufresne, A., Fredette, M., Labonté-LeMoyne, É., & Sénécal, S. (2018). Physiological heatmaps: A tool for visualizing users’ emotional reactions. Multimedia Tools and Applications, 77(9), 11547–11574. https://doi.org/https://doi.org/10.1007/s11042-017-5091-1
- Desmet, P. M., & Hekkert, P. (2009). Special issue editorial: Design & emotion. International Journal of Design, 3(2), 1–6.
- Eger, N., Ball, L. J., Stevens, R., & Dodd, J. (2007). Cueing retrospective verbal reports in usability testing through eye-movement replay. Proceedings of the 21st British HCI Group Annual Conference on People and Computers: HCI … But Not As We Know It - Volume 1 (pp. 129–137). http://dl.acm.org/citation.cfm?id=1531294.1531312
- Epp, C., Lippold, M., & Mandryk, R. L. (2011). Identifying emotional states using keystroke dynamics. Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (pp. 715–724). https://doi.org/https://doi.org/10.1145/1978942.1979046
- Georges, V., Courtemanche, F., Sénécal, S., Léger, P.-M., Nacke, L., & Pourchon, R. (2017). The adoption of physiological measures as an evaluation tool in UX. International Conference on HCI in Business, Government, and Organizations (pp. 90–98).
- Gjoreski, M., Gjoreski, H., Luštrek, M., & Gams, M. (2016). Continuous stress detection using a wrist device: In laboratory and real life. Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing: Adjunct (pp. 1185–1193). https://doi.org/https://doi.org/10.1145/2968219.2968306
- Hanington, B. (2017). Chapter 6—design and emotional experience. In M. Jeon (Ed.), Emotions and affect in human factors and human-computer interaction (pp. 165–183). Academic Press. https://doi.org/https://doi.org/10.1016/B978-0-12-801851-4.00006-9
- Harley, A. (2014). Segment analytics data using personas. Nielsen Norman Group. https://www.nngroup.com/articles/analytics-persona-segment/
- Hassenzahl, M. (2008). User experience (UX): Towards an experiential perspective on product quality. Proceedings of the 20th Conference on L’Interaction Homme-Machine (pp. 11–15). https://doi.org/https://doi.org/10.1145/1512714.1512717
- Hassenzahl, M., & Tractinsky, N. (2006). User experience—A research agenda. Behaviour & Information Technology, 25(2), 91–97. https://doi.org/https://doi.org/10.1080/01449290500330331
- Healey, J. (2000). Wearable and automotive systems for affect recognition from physiology [PhD Thesis]. Massachusetts Institute of Technology.
- Healey, J., & Picard, R. (2005). Detecting stress during real-world driving tasks using physiological sensors. IEEE Transactions on Intelligent Transportation Systems, 6(2), 156–166. https://doi.org/https://doi.org/10.1109/TITS.2005.848368
- Hernandez, J., Paredes, P., Roseway, A., & Czerwinski, M. (2014). Under pressure: Sensing stress of computer users. Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (pp. 51–60). https://doi.org/https://doi.org/10.1145/2556288.2557165
- Hong, J.-H., Ramos, J., & Dey, A. K. (2012). Understanding physiological responses to stressors during physical activity. Proceedings of the 2012 ACM Conference on Ubiquitous Computing (pp. 270–279). https://doi.org/https://doi.org/10.1145/2370216.2370260
- Hornbæk, K., & Law, E. L.-C. (2007). Meta-analysis of correlations among usability measures. Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (pp. 617–626).
- Hovsepian, K., al’Absi, M., Ertin, E., Kamarck, T., Nakajima, M., & Kumar, S. (2015). cStress: Towards a gold standard for continuous stress assessment in the mobile environment. Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing (pp. 493–504). https://doi.org/https://doi.org/10.1145/2750858.2807526
- Jangho, K., Da-Hye, K., Wanjoo, P., & Laehyun, K. (2016). A wearable device for emotional recognition using facial expression and physiological response. Conference Proceedings: … Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference (pp. 5765–5768). https://doi.org/https://doi.org/10.1109/EMBC.2016.7592037
- Khorram, S., Jaiswal, M., Gideon, J., McInnis, M., & Mower Provost, E. (2018). The PRIORI emotion dataset. Linking Mood to Emotion Detected In-the-Wild. Interspeech, 2018, 1903–1907. https://doi.org/https://doi.org/10.21437/Interspeech.2018-2355
- Kivikangas, J. M., Chanel, G., Cowley, B., Ekman, I., Salminen, M., Järvelä, S., & Ravaja, N. (2011). A review of the use of psychophysiological methods in game research. Journal of Gaming & Virtual Worlds, 3(3), 181–199. https://doi.org/https://doi.org/10.1386/jgvw.3.3.181_1
- Koelstra, S., Muhl, C., Soleymani, M., Lee, J.-S., Yazdani, A., Ebrahimi, T., Pun, T., Nijholt, A., & Patras, I. (2012). DEAP: A Database for Emotion Analysis; Using Physiological Signals. IEEE Transactions on Affective Computing, 3(1), 18–31. https://doi.org/https://doi.org/10.1109/T-AFFC.2011.15
- Landis, J. R., & Koch, G. G. (1977). The measurement of observer agreement for categorical data. Biometrics, 33(1), 159–174. https://doi.org/https://doi.org/10.2307/2529310
- Law, E. L.-C., Roto, V., Hassenzahl, M., Vermeeren, A. P. O. S., & Kort, J. (2009). Understanding, scoping and defining user experience: A survey approach. Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (pp. 719–728). https://doi.org/https://doi.org/10.1145/1518701.1518813
- Le Fevre, M., Matheny, J., & Kolt, G. S. (2003). Eustress, distress, and interpretation in occupational stress. Journal of Managerial Psychology, 18(7), 726–744. https://doi.org/https://doi.org/10.1108/02683940310502412
- Lee, E.-H. (2012). Review of the psychometric evidence of the perceived stress scale. Asian Nursing Research, 6(4), 121–127. https://doi.org/https://doi.org/10.1016/j.anr.2012.08.004
- Lee, K., Choi, J., Marakas, G. M., & Singh, S. N. (2019). Two distinct routes for inducing emotions in HCI design. International Journal of Human-Computer Studies, 124, 67–80. https://doi.org/https://doi.org/10.1016/j.ijhcs.2018.11.012
- Lewis, J. R. (2019). Measuring perceived usability: SUS, UMUX, and CSUQ ratings for four everyday products. International Journal of Human–Computer Interaction, 35(15), 1404–1419. https://doi.org/https://doi.org/10.1080/10447318.2018.1533152
- Liapis, A., Karousos, N., Katsanos, C., & Xenos, M. (2014). Evaluating user’s emotional experience in HCI: The physiOBS approach. In M. Kurosu (Ed.), Human-computer interaction. Advanced interaction modalities and techniques (pp. 758–767). Springer International Publishing. http://link.springer.com/chapter/10.1007/978-3-319-07230-2_72
- Liapis, A., Katsanos, C., Sotiropoulos, D., Xenos, M., & Karousos, N. (2015a). Recognizing emotions in human computer interaction: Studying stress using skin conductance. In J. Abascal, S. Barbosa, M. Fetter, T. Gross, P. Palanque, & M. Winckler (Eds.), Human-Computer Interaction – INTERACT 2015 (pp. 255–262). Springer International Publishing. https://doi.org/https://doi.org/10.1007/978-3-319-22701-6_18
- Liapis, A., Katsanos, C., Sotiropoulos, D., Xenos, M., & Karousos, N. (2015b). Subjective assessment of stress in HCI: A study of the valence-arousal scale using skin conductance. Proceedings of the 11th Biannual Conference on Italian SIGCHI Chapter (pp. 174–177). https://doi.org/https://doi.org/10.1145/2808435.2808450
- Liapis, A., Katsanos, C., Sotiropoulos, D. G., Karousos, N., & Xenos, M. (2017). Stress in interactive applications: Analysis of the valence-arousal space based on physiological signals and self-reported data. Multimedia Tools and Applications, 76(4), 5051–5071. https://doi.org/https://doi.org/10.1007/s11042-016-3637-2
- Liapis, A., Katsanos, C., & Xenos, M. (2018). Don’t leave me alone: Retrospective think aloud supported by real-time monitoring of participant’s physiology. In M. Kurosu (Ed.), Human-computer interaction. Theories, methods, and human issues (pp. 148–158). Springer International Publishing.
- Lindholm, J., Backholm, K., & Högväg, J. (2018). What eye movements and facial expressions tell us about user-friendliness: Testing a tool for communicators and journalists. In Social media use in crisis and risk communication: Emergencies, concerns and awareness (pp. 205–225). Emerald Publishing Limited. https://doi.org/https://doi.org/10.1108/978-1-78756-269-120181014
- Lovallo, W. R. (2015). Stress and health: Biological and psychological interactions. Sage publications.
- Lunn, D., & Harper, S. (2010). Using galvanic skin response measures to identify areas of frustration for older web 2.0 users. Proceedings of the 2010 International Cross Disciplinary Conference on Web Accessibility (W4A), 34:1–34: 10. https://doi.org/https://doi.org/10.1145/1805986.1806032
- Mandryk, R. L., & Atkins, M. S. (2007). A fuzzy physiological approach for continuously modeling emotion during interaction with play technologies. International Journal of Human-Computer Studies, 65(4), 329–347. https://doi.org/https://doi.org/10.1016/j.ijhcs.2006.11.011
- Mandryk, R. L., Atkins, M. S., & Inkpen, K. M. (2006). A continuous and objective evaluation of emotional experience with interactive play environments. Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (pp. 1027–1036). https://doi.org/https://doi.org/10.1145/1124772.1124926
- Mao, Q., Dong, M., Huang, Z., & Zhan, Y. (2014). Learning salient features for speech emotion recognition using convolutional neural networks. IEEE Transactions on Multimedia, 16(8), 2203–2213. https://doi.org/https://doi.org/10.1109/TMM.2014.2360798
- Marshall, C., & Rossman, G. B. (2014). Designing qualitative research. Sage publications.
- McCarthy, J., & Wright, P. (2007). Technology as experience. MIT press.
- Monk, A., Hassenzahl, M., Blythe, M., & Reed, D. (2002). Funology: Designing enjoyment. CHI’02 Extended Abstracts on Human Factors in Computing Systems (pp. 924–925).
- Myroniv, B., Wu, C.-W., Ren, Y., Christian, A., Bajo, E., & Tseng, Y.-C. (2017). Analyzing user emotions via physiology signals. Data Sci Pattern Recog, 1(2), 11–25.
- Nahin, A. F. M. N. H., Alam, J. M., Mahmud, H., & Hasan, K. (2014). Identifying emotion by keystroke dynamics and text pattern analysis. Behaviour & Information Technology, 33(9), 987–996. https://doi.org/https://doi.org/10.1080/0144929X.2014.907343
- Norman, D. (2005). Emotional design: Why we love (or hate) everyday things (1st ed.). Basic Books.
- O’Brien, H. L., & Toms, E. G. (2010). Is there a universal instrument for measuring interactive information retrieval?: The case of the user engagement scale. Proceedings of the Third Symposium on Information Interaction in Context (pp. 335–340). https://doi.org/https://doi.org/10.1145/1840784.1840835
- Obrist, M., Ranasinghe, N., & Spence, C. (2017). Special issue: Multisensory human–computer interaction. International Journal of Human-Computer Studies, 107, 1–4. https://doi.org/https://doi.org/10.1016/j.ijhcs.2017.06.002
- Pakarinen, T., Pietilä, J., & Nieminen, H. (2019). Prediction of self-perceived stress and arousal based on electrodermal activity*. 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) (pp. 2191–2195). https://doi.org/https://doi.org/10.1109/EMBC.2019.8857621
- Petrantonakis, P. C., & Hadjileontiadis, L. J. (2010). Emotion recognition from brain signals using hybrid adaptive filtering and higher order crossings analysis. IEEE Transactions on Affective Computing, 1(2), 81–97. https://doi.org/https://doi.org/10.1109/T-AFFC.2010.7
- Pickering, T. G. (2001). Mental stress as a causal factor in the development of hypertension and cardiovascular disease. Current Hypertension Reports, 3(3), 249–254. https://doi.org/https://doi.org/10.1007/s11906-001-0047-1
- Remy, C., Bates, O., Dix, A., Thomas, V., Hazas, M., Friday, A., & Huang, E. M. (2018). Evaluation beyond usability: Validating sustainable HCI research. Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems, 216:1–216: 14. https://doi.org/https://doi.org/10.1145/3173574.3173790
- Russell, J. A., Weiss, A., & Mendelsohn, G. A. (1989). Affect grid: A single-item scale of pleasure and arousal. Journal of Personality and Social Psychology, 57(3), 493–502. https://doi.org/https://doi.org/10.1037/0022-3514.57.3.493
- Russo, J. E., Johnson, E. J., & Stephens, D. L. (1989). The validity of verbal protocols. Memory & Cognition, 17(6), 759–769. https://doi.org/https://doi.org/10.3758/BF03202637
- Sauro, J., & Lewis, J. R. (2016). Quantifying the user experience: Practical statistics for user research. Morgan Kaufmann.
- Setz, C., Arnrich, B., Schumm, J., Marca, R. L., Tröster, G., & Ehlert, U. (2010). Discriminating stress from cognitive load using a wearable EDA device. IEEE Transactions on Information Technology in Biomedicine, 14(2), 410–417. https://doi.org/https://doi.org/10.1109/TITB.2009.2036164
- Tarnowski, P., Kołodziej, M., Majkowski, A., & Rak, R. J. (2017). Emotion recognition using facial expressions. Procedia Computer Science, 108, 1175–1184. https://doi.org/https://doi.org/10.1016/j.procs.2017.05.025
- Teague, R., De Jesus, K., & Ueno, M. N. (2001). Concurrent vs. Post-task usability test ratings. CHI ’01 Extended Abstracts on Human Factors in Computing Systems (pp. 289–290). https://doi.org/https://doi.org/10.1145/634067.634238
- Verma, G. K., & Tiwary, U. S. (2014). Multimodal fusion framework: A multiresolution approach for emotion classification and recognition from physiological signals. NeuroImage, 102(Part 1), 162–172. https://doi.org/https://doi.org/10.1016/j.neuroimage.2013.11.007
- Wang, S., Liu, Z., Zhu, Y., He, M., Chen, X., & Ji, Q. (2014). Implicit video emotion tagging from audiences’ facial expression. Multimedia Tools and Applications, 74(13), 4679–4706. https://doi.org/https://doi.org/10.1007/s11042-013-1830-0
- Wilson, G. M., & Sasse, M. A. (2000). Investigating the impact of audio degradations on users: Subjective vs objective assessment methods. CHISIG.
- Zong, C., & Chetouani, M. (2009). Hilbert-Huang transform based physiological signals analysis for emotion recognition. 2009 IEEE International Symposium on Signal Processing and Information Technology (ISSPIT) (pp. 334–339).