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        <title type="main" level="a">Application of Smart Technologies for Assessing Users’ Well-Being for Immersive Design Strategies: A State-of-the-Art Review</title>
        <author>
          <persName n="1" ref="https://orcid.org/0000-0003-2880-269X" type="ORCID">
            <forename>Eleonora</forename>
            <surname>D'Ascenzi</surname>
            <placeName type="affiliation">University of Florence, Italy</placeName>
          </persName>
          <persName n="2" ref="https://orcid.org/0000-0001-8470-2648" type="ORCID">
            <forename>Vito</forename>
            <surname>Getuli</surname>
            <placeName type="affiliation">University of Florence, Italy</placeName>
          </persName>
          <persName n="3" ref="https://orcid.org/0000-0003-4724-286X" type="ORCID">
            <forename>Irene</forename>
            <surname>Fiesoli</surname>
            <placeName type="affiliation">University of Florence, Italy</placeName>
          </persName>
        </author>
        <respStmt>
          <resp>This is a section of <title>CONVR 2023 - Proceedings of the 23rd International Conference on  Construction Applications of Virtual Reality </title>(DOI: <idno type="DOI">10.36253/979-12-215-0289-3</idno>) by </resp>
          <name>Pietro Capone, Vito Getuli, Farzad Pour Rahimian, Nashwan Dawood, Alessandro Bruttini, Tommaso Sorbi</name>
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      <publicationStmt>
        <publisher>Firenze University Press</publisher>
        <pubPlace>Florence</pubPlace>
        <date when="2023">2023</date>
        <idno type="DOI">https://doi.org/10.36253/10.36253/979-12-215-0289-3.09</idno>
        <availability>
          <p>Available for academic research purposes</p>
          <p>Open Access</p>
          <p>Copyright Author(s)</p>
          <licence source="text" target="https://creativecommons.org/licenses/by-nc/4.0/legalcode">
            <p>Content licence CC BY-NC 4.0</p>
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          <licence source="metadata" target="https://creativecommons.org/publicdomain/zero/1.0/legalcode">
            <p>Metadata licence CC0 1.0</p>
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        <p>This is original content, published for academic research purposes</p>
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      <abstract xml:lang="en">
        <p>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</p>
      </abstract>
      <textClass>
        <keywords>
          <list>
            <item>extended reality</item>
            <item>virtual reality</item>
            <item>neuro-design</item>
            <item>digital design</item>
            <item>immersive experience</item>
            <item>user experience</item>
            <item>well-being assessment</item>
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        </keywords>
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      <p>It is available online at https://doi.org/10.36253/10.36253/979-12-215-0289-3.09<ref target="https://doi.org/10.36253/10.36253/979-12-215-0289-3.09" /></p>
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        <listBibl>
          <head>References</head>
          <bibl n="138016">
            <bibl>Abburi, H., Shrivastava, M., &amp;amp; Gangashetty, S. V. (2016). Improved Multimodal Sentiment Detection Using Stressed Regions of Audio. PROCEEDINGS OF THE 2016 IEEE REGION 10 CONFERENCE (TENCON), 2834–2837.</bibl>
            <idno type="DOI">10.1109/TENCON.2016.7848560</idno>
          </bibl>
          <bibl n="136682">
            <bibl>Acerbi, G., Rovini, E., Betti, S., Tirri, A., Ronai, J. F., Sirianni, A., Agrimi, J., Eusebi, L., &amp;amp; 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).</bibl>
            <idno type="DOI">10.1007/978-3-319-54283-6\_3</idno>
          </bibl>
          <bibl n="138814">
            <bibl>Affanni, A., Bernardini, R., Piras, A., Rinaldo, R., &amp;amp; Zontone, P. (2018). Driver’s stress detection using Skin Potential Response signals. MEASUREMENT, 122, 264–274.</bibl>
            <idno type="DOI">10.1016/j.measurement.2018.03.040</idno>
          </bibl>
          <bibl n="137770">
            <bibl>Al Abdi, R. M., Alhitary, A. E., Abdul Hay, E. W., &amp;amp; Al-Bashir, A. K. (2018). Objective detection of chronic stress using physiological parameters. Medical &amp;amp; Biological Engineering &amp;amp; Computing, 56(12), 2273–2286.</bibl>
            <idno type="DOI">10.1007/s11517-018-1854-8</idno>
          </bibl>
          <bibl n="138884">
            <bibl>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.</bibl>
            <idno type="DOI">10.1108/ARCH-10-2020-0249</idno>
          </bibl>
          <bibl n="136642">
            <bibl>Amerio, A., Brambilla, A., Morganti, A., Aguglia, A., Bianchi, D., Santi, F., Costantini, L., Odone, A., Costanza, A., Signorelli, C., Serafini, G., Amore, M., &amp;amp; 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.</bibl>
            <idno type="DOI">10.3390/ijerph17165973</idno>
          </bibl>
          <bibl n="136814">
            <bibl>Anusha, A. S., Sukumaran, P., Sarveswaran, V., Surees Kumar, S., Shyam, A., Akl, T. J., Preejith, S. P., &amp;amp; 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.</bibl>
            <idno type="DOI">10.1109/JBHI.2019.2893222</idno>
          </bibl>
          <bibl n="138607">
            <bibl>Attallah, O. (2020). An Effective Mental Stress State Detection and Evaluation System Using Minimum Number of Frontal Brain Electrodes. Diagnostics (Basel, Switzerland), 10(5).</bibl>
            <idno type="DOI">10.3390/diagnostics10050292</idno>
          </bibl>
          <bibl n="136774">
            <bibl>Bin, M. S., Khalifa, O. O., &amp;amp; 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)</bibl>
            <idno type="DOI">10.1109/ICCNEEE.2015.7381390</idno>
          </bibl>
          <bibl n="137696">
            <bibl>Burton, E. J., Mitchell, L., &amp;amp; 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.</bibl>
            <idno type="DOI">10.1186/1471-2458-11-839</idno>
          </bibl>
          <bibl n="137931">
            <bibl>Can, Y. S., Arnrich, B., &amp;amp; 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.</bibl>
            <idno type="DOI">10.1016/j.jbi.2019.103139</idno>
          </bibl>
          <bibl n="137044">
            <bibl>Debard, G., De Witte, N., Sels, R., Mertens, M., Van Daele, T., &amp;amp; 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.</bibl>
            <idno type="DOI">10.1155/2020/8846077</idno>
          </bibl>
          <bibl n="138143">
            <bibl>Delmastro, F., Martino, F. D., &amp;amp; Dolciotti, C. (2020). Cognitive Training and Stress Detection in MCI Frail Older People through Wearable Sensors and Machine Learning. IEEE Access, 8, 65573–65590.</bibl>
            <idno type="DOI">10.1109/ACCESS.2020.2985301</idno>
          </bibl>
          <bibl n="138885">
            <bibl>Elzeiny, S., &amp;amp; Qaraqe, M. (2018). Blueprint to Workplace Stress Detection Approaches. 2018 INTERNATIONAL CONFERENCE ON COMPUTER AND APPLICATIONS (ICCA), 407–412.</bibl>
            <idno type="DOI">10.1109/COMAPP.2018.8460293</idno>
          </bibl>
          <bibl n="138730">
            <bibl>Feng, Z., Li, N., Feng, L., Chen, D., &amp;amp; Zhu, C. (2021). Leveraging ECG signals and social media for stress detection. BEHAVIOUR \&amp;amp; INFORMATION TECHNOLOGY, 40(2), 116–133.</bibl>
            <idno type="DOI">10.1080/0144929X.2019.1673820</idno>
          </bibl>
          <bibl n="137859">
            <bibl>Ghaderi, A., Frounchi, J., &amp;amp; Farnam, A. (2015). Machine Learning-based Signal Processing Using Physiological Signals for Stress Detection. 2015 22ND IRANIAN CONFERENCE ON BIOMEDICAL ENGINEERING (ICBME), 93–98.</bibl>
            <idno type="DOI">10.1109/ICBME.2015.7404123</idno>
          </bibl>
          <bibl n="137419">
            <bibl>Gjoreski, M., Gjoreski, H., Lutrek, M., &amp;amp; Gams, M. (2015). Automatic Detection of Perceived Stress in Campus Students Using Smartphones. Proceedings - 2015 International Conference on Intelligent Environments, IE 2015, 132–135.</bibl>
            <idno type="DOI">10.1109/IE.2015.27</idno>
          </bibl>
          <bibl n="136857">
            <bibl>Gunawardhane, S. D. W., De Silva, P. M., Kulathunga, D. S. B., &amp;amp; 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</bibl>
            <idno type="DOI">10.1109/ICTer.2013.6761185</idno>
          </bibl>
          <bibl n="136615">
            <bibl>Healy, M., Donovan, R., Walsh, P., &amp;amp; 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)</bibl>
            <idno type="DOI">10.1109/BIBM.2018.8621562</idno>
          </bibl>
          <bibl n="138285">
            <bibl>Kalas, M. S., &amp;amp; Momin, B. F. (2016). Stress Detection and Reduction using EEG Signals. 2016 INTERNATIONAL CONFERENCE ON ELECTRICAL, ELECTRONICS, AND OPTIMIZATION TECHNIQUES (ICEEOT), 471–475</bibl>
            <idno type="DOI">10.1109/ICEEOT.2016.7755604</idno>
          </bibl>
          <bibl n="136732">
            <bibl>Kalimeri, K., &amp;amp; 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).</bibl>
            <idno type="DOI">10.1145/2993148.2993159</idno>
          </bibl>
          <bibl n="138091">
            <bibl>Melone, M. R. S., &amp;amp; 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.</bibl>
            <idno type="DOI">10.6092/1970-9870/6923</idno>
          </bibl>
          <bibl n="138554">
            <bibl>Minguillon, J., Perez, E., Lopez-Gordo, M. A., Pelayo, F., &amp;amp; Sanchez-Carrion, M. J. (2018). Portable system for real-time detection of stress level. Sensors (Switzerland), 18(8).</bibl>
            <idno type="DOI">10.3390/s18082504</idno>
          </bibl>
          <bibl n="137443">
            <bibl>Mozos, O. M., Sandulescu, V., Andrews, S., Ellis, D., Bellotto, N., Dobrescu, R., &amp;amp; Ferrandez, J. M. (2017). Stress detection using wearable physiological and sociometric sensors. International Journal of Neural Systems, 27(2).</bibl>
            <idno type="DOI">10.1142/S0129065716500416</idno>
          </bibl>
          <bibl n="137678">
            <bibl>Pandey, P., Lee, E. K., &amp;amp; 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.</bibl>
            <idno type="DOI">10.1109/JBHI.2015.2477342</idno>
          </bibl>
          <bibl n="137616">
            <bibl>Pascoe, M. C., Thompson, D. R., &amp;amp; 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.</bibl>
            <idno type="DOI">10.1016/j.psyneuen.2017.08.008</idno>
          </bibl>
          <bibl n="137029">
            <bibl>Qiao, S., Li, X., Zilioli, S., Chen, Z., Deng, H., Pan, J., &amp;amp; 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).</bibl>
            <idno type="DOI">10.1371/journal.pone.0169827</idno>
          </bibl>
          <bibl n="137679">
            <bibl>Rachakonda, L., Mohanty, S. P., Kougianos, E., &amp;amp; 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.</bibl>
            <idno type="DOI">10.1109/TCE.2019.2940472</idno>
          </bibl>
          <bibl n="138632">
            <bibl>Rani, P., Sims, J., Brackin, R., &amp;amp; Sarkar, N. (2002). Online stress detection using psychophysiological signals for implicit human-robot cooperation. ROBOTICA, 20(6), 673–685.</bibl>
            <idno type="DOI">10.1017/S0263574702004484</idno>
          </bibl>
          <bibl n="137680">
            <bibl>Reanaree, P., Tananchana, P., Narongwongwathana, W., &amp;amp; Pintavirooj, C. (2016). Stress and Office-Syndrome Detection using EEG, HRV and Hand Movement. 2016 9TH BIOMEDICAL ENGINEERING INTERNATIONAL CONFERENCE (BMEICON)</bibl>
            <idno type="DOI">10.1109/BMEiCON.2016.7859624</idno>
          </bibl>
          <bibl n="138231">
            <bibl>Sağbaş, E. A., Korukoglu, S., &amp;amp; Balli, S. (2020). Stress Detection via Keyboard Typing Behaviors by Using Smartphone Sensors and Machine Learning Techniques. Journal of Medical Systems, 44(4).</bibl>
            <idno type="DOI">10.1007/s10916-020-1530-z</idno>
          </bibl>
          <bibl n="139263">
            <bibl>Sriramprakash, S., Prasanna, V. D., &amp;amp; Murthy, O. V. R. (2017a). Stress Detection in Working People. Procedia Computer Science, 115, 359–366.</bibl>
            <idno type="DOI">10.1016/j.procs.2017.09.090</idno>
          </bibl>
          <bibl n="138062">
            <bibl>Sriramprakash, S., Prasanna, V. D., &amp;amp; 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.</bibl>
            <idno type="DOI">10.1016/j.procs.2017.09.090</idno>
          </bibl>
          <bibl n="138063">
            <bibl>Vizer, L. M., Zhou, L., &amp;amp; Sears, A. (2009). Automated stress detection using keystroke and linguistic features: An exploratory study. INTERNATIONAL JOURNAL OF HUMAN-COMPUTER STUDIES, 67(10), 870–886.</bibl>
            <idno type="DOI">10.1016/j.ijhcs.2009.07.005</idno>
          </bibl>
          <bibl n="136690">
            <bibl>Wells, S., Tremblay, P. F., Flynn, A., Russell, E., Kennedy, J., Rehm, J., Van Uum, S., Koren, G., &amp;amp; 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.</bibl>
            <idno type="DOI">10.3109/10253890.2014.930432</idno>
          </bibl>
          <bibl n="136620">
            <bibl>Zalabarria, U., Irigoyen, E., Martinez, R., &amp;amp; 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).</bibl>
            <idno type="DOI">10.1007/978-3-319-47364-2\_29</idno>
          </bibl>
          <bibl n="139188">
            <bibl>Zhang, H., Feng, L., Li, N., Jin, Z., &amp;amp; Cao, L. (2020a). Video-based stress detection through deep learning. Sensors (Switzerland), 20(19), 1–17.</bibl>
            <idno type="DOI">10.3390/s20195552</idno>
          </bibl>
          <bibl n="139413">
            <bibl>Zhang, H., Feng, L., Li, N., Jin, Z., &amp;amp; Cao, L. (2020b). Video-Based Stress Detection through Deep Learning. SENSORS, 20(19).</bibl>
            <idno type="DOI">10.3390/s20195552</idno>
          </bibl>
          <bibl n="138319">
            <bibl>Zhao, L., Li, Q., Xue, Y., Jia, J., &amp;amp; Feng, L. (2016). A systematic exploration of the micro-blog feature space for teens stress detection. Health Information Science and Systems, 4(1), 3.</bibl>
            <idno type="DOI">10.1186/s13755-016-0016-3</idno>
          </bibl>
        </listBibl>
      </div>
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