1. Abburi, H., Shrivastava, M., & Gangashetty, S. V. (2016). Improved Multimodal Sentiment Detection Using Stressed Regions of Audio. PROCEEDINGS OF THE 2016 IEEE REGION 10 CONFERENCE (TENCON), 2834–2837. DOI: 10.1109/TENCON.2016.7848560
2. Acerbi, G., Rovini, E., Betti, S., Tirri, A., Ronai, J. F., Sirianni, A., Agrimi, J., Eusebi, L., & Cavallo, F. (2017). A Wearable System for Stress Detection Through Physiological Data Analysis. In Cavallo, F and Marletta, V and Monteriu, A and Siciliano, P (Ed.), AMBIENT ASSISTED LIVING (Vol. 426, pp. 31–50). DOI: 10.1007/978-3-319-54283-6\_3
3. Affanni, A., Bernardini, R., Piras, A., Rinaldo, R., & Zontone, P. (2018). Driver’s stress detection using Skin Potential Response signals. MEASUREMENT, 122, 264–274. DOI: 10.1016/j.measurement.2018.03.040
4. Al Abdi, R. M., Alhitary, A. E., Abdul Hay, E. W., & Al-Bashir, A. K. (2018). Objective detection of chronic stress using physiological parameters. Medical & Biological Engineering & Computing, 56(12), 2273–2286. DOI: 10.1007/s11517-018-1854-8
5. Alraouf, A. A. (2021). The new normal or the forgotten normal: contesting COVID-19 impact on contemporary architecture and urbanism. Archnet-IJAR, 15(1), 167–188. DOI: 10.1108/ARCH-10-2020-0249
6. Amerio, A., Brambilla, A., Morganti, A., Aguglia, A., Bianchi, D., Santi, F., Costantini, L., Odone, A., Costanza, A., Signorelli, C., Serafini, G., Amore, M., & Capolongo, S. (2020). Covid-19 lockdown: Housing built environment’s effects on mental health. International Journal of Environmental Research and Public Health, 17(16), 1–10. DOI: 10.3390/ijerph17165973
7. Anusha, A. S., Sukumaran, P., Sarveswaran, V., Surees Kumar, S., Shyam, A., Akl, T. J., Preejith, S. P., & Sivaprakasam, M. (2020). Electrodermal Activity Based Pre-surgery Stress Detection Using a Wrist Wearable. IEEE Journal of Biomedical and Health Informatics, 24(1), 92–100. DOI: 10.1109/JBHI.2019.2893222
8. Attallah, O. (2020). An Effective Mental Stress State Detection and Evaluation System Using Minimum Number of Frontal Brain Electrodes. Diagnostics (Basel, Switzerland), 10(5). DOI: 10.3390/diagnostics10050292
9. Bin, M. S., Khalifa, O. O., & Saeed, R. A. (2015). Real-Time Personalized Stress Detection from Physiological Signals. In Saeed, RA and Mokhtar, RA (Ed.), 2015 International Conference on Computing, Control, Networking, Electronics and Embedded Systems Engineering (ICCNEEE) (pp. 352–356) DOI: 10.1109/ICCNEEE.2015.7381390
10. Burton, E. J., Mitchell, L., & Stride, C. B. (2011). Good places for ageing in place: Development of objective built environment measures for investigating links with older people’s wellbeing. BMC Public Health, 11. DOI: 10.1186/1471-2458-11-839
11. Can, Y. S., Arnrich, B., & Ersoy, C. (2019). Stress detection in daily life scenarios using smart phones and wearable sensors: A survey. In Journal of Biomedical Informatics (Vol. 92). Academic Press Inc. DOI: 10.1016/j.jbi.2019.103139
12. Debard, G., De Witte, N., Sels, R., Mertens, M., Van Daele, T., & Bonroy, B. (2020). Making Wearable Technology Available for Mental Healthcare through an Online Platform with Stress Detection Algorithms: The Carewear Project. JOURNAL OF SENSORS, 2020. DOI: 10.1155/2020/8846077
13. Delmastro, F., Martino, F. D., & Dolciotti, C. (2020). Cognitive Training and Stress Detection in MCI Frail Older People through Wearable Sensors and Machine Learning. IEEE Access, 8, 65573–65590. DOI: 10.1109/ACCESS.2020.2985301
14. Elzeiny, S., & Qaraqe, M. (2018). Blueprint to Workplace Stress Detection Approaches. 2018 INTERNATIONAL CONFERENCE ON COMPUTER AND APPLICATIONS (ICCA), 407–412. DOI: 10.1109/COMAPP.2018.8460293
15. Feng, Z., Li, N., Feng, L., Chen, D., & Zhu, C. (2021). Leveraging ECG signals and social media for stress detection. BEHAVIOUR \& INFORMATION TECHNOLOGY, 40(2), 116–133. DOI: 10.1080/0144929X.2019.1673820
16. Ghaderi, A., Frounchi, J., & Farnam, A. (2015). Machine Learning-based Signal Processing Using Physiological Signals for Stress Detection. 2015 22ND IRANIAN CONFERENCE ON BIOMEDICAL ENGINEERING (ICBME), 93–98. DOI: 10.1109/ICBME.2015.7404123
17. Gjoreski, M., Gjoreski, H., Lutrek, M., & Gams, M. (2015). Automatic Detection of Perceived Stress in Campus Students Using Smartphones. Proceedings - 2015 International Conference on Intelligent Environments, IE 2015, 132–135. DOI: 10.1109/IE.2015.27
18. Gunawardhane, S. D. W., De Silva, P. M., Kulathunga, D. S. B., & Arunatileka, S. M. K. D. (2013). Non Invasive Human Stress Detection Using Key Stroke Dynamics and Pattern Variations. 2013 INTERNATIONAL CONFERENCE ON ADVANCES IN ICT FOR EMERGING REGIONS (ICTER), 240–247 DOI: 10.1109/ICTer.2013.6761185
19. Healy, M., Donovan, R., Walsh, P., & Zheng, H. (2018). A Machine Learning Emotion Detection Platform to Support Affective Well Being. In Zheng, H and Callejas, Z and Griol, D and Wang, H and Hu, X and Schmidt, H and Baumbach, J and Dickerson, J and Zhang, L (Ed.), PROCEEDINGS 2018 IEEE INTERNATIONAL CONFERENCE ON BIOINFORMATICS AND BIOMEDICINE (BIBM) (pp. 2694–2700) DOI: 10.1109/BIBM.2018.8621562
20. Kalas, M. S., & Momin, B. F. (2016). Stress Detection and Reduction using EEG Signals. 2016 INTERNATIONAL CONFERENCE ON ELECTRICAL, ELECTRONICS, AND OPTIMIZATION TECHNIQUES (ICEEOT), 471–475 DOI: 10.1109/ICEEOT.2016.7755604
21. Kalimeri, K., & Saitis, C. (2016). Exploring Multimodal Biosignal Features for Stress Detection during Indoor Mobility. In Nakano, YI and Andre, E and Nishida, T and Busso, C and Pelachaud, C (Ed.), ICMI’16: PROCEEDINGS OF THE 18TH ACM INTERNATIONAL CONFERENCE ON MULTIMODAL INTERACTION (pp. 53–60). DOI: 10.1145/2993148.2993159
22. Melone, M. R. S., & Borgo, S. (2020). Rethinking rules and social practices. The design of urban spaces in the post-Covid-19 lockdown. TEMA-JOURNAL OF LAND USE MOBILITY AND ENVIRONMENT, SI, 333–341. DOI: 10.6092/1970-9870/6923
23. Minguillon, J., Perez, E., Lopez-Gordo, M. A., Pelayo, F., & Sanchez-Carrion, M. J. (2018). Portable system for real-time detection of stress level. Sensors (Switzerland), 18(8). DOI: 10.3390/s18082504
24. Mozos, O. M., Sandulescu, V., Andrews, S., Ellis, D., Bellotto, N., Dobrescu, R., & Ferrandez, J. M. (2017). Stress detection using wearable physiological and sociometric sensors. International Journal of Neural Systems, 27(2). DOI: 10.1142/S0129065716500416
25. Pandey, P., Lee, E. K., & Pompili, D. (2016). A Distributed Computing Framework for Real-Time Detection of Stress and of Its Propagation in a Team. IEEE Journal of Biomedical and Health Informatics, 20(6), 1502–1512. DOI: 10.1109/JBHI.2015.2477342
26. Pascoe, M. C., Thompson, D. R., & Ski, C. F. (2017). Yoga, mindfulness-based stress reduction and stress-related physiological measures: A meta-analysis. In Psychoneuroendocrinology (Vol. 86, pp. 152–168). Elsevier Ltd. DOI: 10.1016/j.psyneuen.2017.08.008
27. Qiao, S., Li, X., Zilioli, S., Chen, Z., Deng, H., Pan, J., & Guo, W. (2017). Hair measurements of cortisol, DHEA, and DHEA to cortisol ratio as biomarkers of chronic stress among people living with HIV in China: Known-group validation. PLoS ONE, 12(1). DOI: 10.1371/journal.pone.0169827
28. Rachakonda, L., Mohanty, S. P., Kougianos, E., & Sundaravadivel, P. (2019). Stress-Lysis: A DNN-Integrated Edge Device for Stress Level Detection in the IoMT. IEEE TRANSACTIONS ON CONSUMER ELECTRONICS, 65(4), 474–483. DOI: 10.1109/TCE.2019.2940472
29. Rani, P., Sims, J., Brackin, R., & Sarkar, N. (2002). Online stress detection using psychophysiological signals for implicit human-robot cooperation. ROBOTICA, 20(6), 673–685. DOI: 10.1017/S0263574702004484
30. Reanaree, P., Tananchana, P., Narongwongwathana, W., & Pintavirooj, C. (2016). Stress and Office-Syndrome Detection using EEG, HRV and Hand Movement. 2016 9TH BIOMEDICAL ENGINEERING INTERNATIONAL CONFERENCE (BMEICON) DOI: 10.1109/BMEiCON.2016.7859624
31. Sağbaş, E. A., Korukoglu, S., & Balli, S. (2020). Stress Detection via Keyboard Typing Behaviors by Using Smartphone Sensors and Machine Learning Techniques. Journal of Medical Systems, 44(4). DOI: 10.1007/s10916-020-1530-z
32. Sriramprakash, S., Prasanna, V. D., & Murthy, O. V. R. (2017a). Stress Detection in Working People. Procedia Computer Science, 115, 359–366. DOI: 10.1016/j.procs.2017.09.090
33. Sriramprakash, S., Prasanna, V. D., & Murthy, O. V. R. (2017b). Stress Detection in Working People. In G. M. Paul Mulerikkal J. (Ed.), Procedia Computer Science (Vol. 115, pp. 359–366). Elsevier B.V. DOI: 10.1016/j.procs.2017.09.090
34. Vizer, L. M., Zhou, L., & Sears, A. (2009). Automated stress detection using keystroke and linguistic features: An exploratory study. INTERNATIONAL JOURNAL OF HUMAN-COMPUTER STUDIES, 67(10), 870–886. DOI: 10.1016/j.ijhcs.2009.07.005
35. Wells, S., Tremblay, P. F., Flynn, A., Russell, E., Kennedy, J., Rehm, J., Van Uum, S., Koren, G., & Graham, K. (2014). Associations of hair cortisol concentration with self-reported measures of stress and mental health-related factors in a pooled database of diverse community samples. Stress, 17(4), 334–342. DOI: 10.3109/10253890.2014.930432
36. Zalabarria, U., Irigoyen, E., Martinez, R., & Salazar-Ramirez, A. (2017). Detection of Stress Level and Phases by Advanced Physiological Signal Processing Based on Fuzzy Logic. In Grana, M and LopezGuede, JM and Etxaniz, O and Herrero, A and Quintian, H and Corchado, E (Ed.), INTERNATIONAL JOINT CONFERENCE SOCO’16- CISIS’16-ICEUTE’16 (Vol. 527, pp. 301–312). DOI: 10.1007/978-3-319-47364-2\_29
37. Zhang, H., Feng, L., Li, N., Jin, Z., & Cao, L. (2020a). Video-based stress detection through deep learning. Sensors (Switzerland), 20(19), 1–17. DOI: 10.3390/s20195552
38. Zhang, H., Feng, L., Li, N., Jin, Z., & Cao, L. (2020b). Video-Based Stress Detection through Deep Learning. SENSORS, 20(19). DOI: 10.3390/s20195552
39. Zhao, L., Li, Q., Xue, Y., Jia, J., & Feng, L. (2016). A systematic exploration of the micro-blog feature space for teens stress detection. Health Information Science and Systems, 4(1), 3. DOI: 10.1186/s13755-016-0016-3

Chapter Information

Bibliographic Information