Contained in:
Book Chapter

Application of Smart Technologies for Assessing Users’ Well-Being for Immersive Design Strategies: A State-of-the-Art Review

  • Eleonora D'Ascenzi
  • Vito Getuli
  • Irene Fiesoli

As never before, during the COVID-19 pandemic, the effectiveness of the digital design strategies on the user’s well-being has been questioned. However, a research branch astride digital design and neuroscience able to overcome net discipline borders to analyse users’ well-being seems to be lacking. Today mainly qualitative data are used in the design field for the investigation of users’ quality experience. Although fundamental, they also have great disadvantages such as unanswered questions, unconscientious responses, and respondents’ biases. As such, a systematic state of art review is presented to find methodologies and tools currently used in medicine to identify the impact of digital design strategies (XR) on users’ well-being through quantitative and objective data. The main technologies used for this purpose have been synthesized in a schematic chart by reporting the principal related biometric data (skin conductivity, heart rate metrics and breathing rates), as well as other technologies such as video/images/audio analysis based on sensors and machine learning to reach out mass numbers. In conclusion, gaps and future applications of this innovative approach within the virtual environment have been identified by the authors

  • Keywords:
  • extended reality,
  • virtual reality,
  • neuro-design,
  • digital design,
  • immersive experience,
  • user experience,
  • well-being assessment,
+ Show More

Eleonora D'Ascenzi

University of Florence, Italy - ORCID: 0000-0003-2880-269X

Vito Getuli

University of Florence, Italy - ORCID: 0000-0001-8470-2648

Irene Fiesoli

University of Florence, Italy - ORCID: 0000-0003-4724-286X

  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
PDF
  • Publication Year: 2023
  • Pages: 91-99

XML
  • Publication Year: 2023

Chapter Information

Chapter Title

Application of Smart Technologies for Assessing Users’ Well-Being for Immersive Design Strategies: A State-of-the-Art Review

Authors

Eleonora D'Ascenzi, Vito Getuli, Irene Fiesoli

DOI

10.36253/979-12-215-0289-3.09

Peer Reviewed

Publication Year

2023

Copyright Information

© 2023 Author(s)

Content License

CC BY-NC 4.0

Metadata License

CC0 1.0

Bibliographic Information

Book Title

CONVR 2023 - Proceedings of the 23rd International Conference on Construction Applications of Virtual Reality

Book Subtitle

Managing the Digital Transformation of Construction Industry

Editors

Pietro Capone, Vito Getuli, Farzad Pour Rahimian, Nashwan Dawood, Alessandro Bruttini, Tommaso Sorbi

Peer Reviewed

Publication Year

2023

Copyright Information

© 2023 Author(s)

Content License

CC BY-NC 4.0

Metadata License

CC0 1.0

Publisher Name

Firenze University Press

DOI

10.36253/979-12-215-0289-3

eISBN (pdf)

979-12-215-0289-3

eISBN (xml)

979-12-215-0257-2

Series Title

Proceedings e report

Series ISSN

2704-601X

Series E-ISSN

2704-5846

61

Fulltext
downloads

66

Views

Export Citation

1,310

Open Access Books

in the Catalogue

1,977

Book Chapters

3,390,281

Fulltext
downloads

4,172

Authors

from 873 Research Institutions

of 64 Nations

63

scientific boards

from 340 Research Institutions

of 43 Nations

1,157

Referees

from 344 Research Institutions

of 37 Nations