{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,10]],"date-time":"2026-07-10T12:53:08Z","timestamp":1783687988032,"version":"3.55.0"},"reference-count":74,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2026,4,10]],"date-time":"2026-04-10T00:00:00Z","timestamp":1775779200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Chronic stress is a time-dependent condition characterized by sustained dysregulation across neural, autonomic, and endocrine systems, with important consequences for both health and socioeconomic outcomes. Unlike acute stress, which is typically characterized by short-lived physiological activation, chronic stress reflects an accumulated allostatic load and a longer-term recalibration of stress response systems. Recent advances in physiological sensing and artificial intelligence (AI) have supported the development of computational approaches for chronic stress detection using electroencephalography (EEG), heart rate variability (HRV), photoplethysmography (PPG), electrodermal activity (EDA), and wearable multimodal platforms. This narrative review examines current AI-based studies through three main inferential paradigms: resting baseline dysregulation, longitudinal physiological monitoring, and reactivity-based inference. Across modalities, classical machine learning (ML) methods, particularly support vector machines (SVMs) and tree-based ensembles, remain the most commonly used approaches, largely because available datasets are small and most pipelines still depend on engineered features. Deep learning (DL) methods are beginning to emerge, but their use remains constrained by the lack of large, standardized, longitudinal datasets specifically designed for chronic stress research. Major challenges include ambiguity in stress labeling, limited longitudinal validation, circadian confounding, inter-individual variability, and small cohort sizes. Future progress will depend on standardized datasets, biologically grounded multimodal integration, hybrid baseline-reactivity modeling, adaptive personalization, and more interpretable AI systems. Greater emphasis is also needed on clinical relevance and generalizability if AI-based chronic stress monitoring is to move beyond experimental settings.<\/jats:p>","DOI":"10.3390\/s26082345","type":"journal-article","created":{"date-parts":[[2026,4,10]],"date-time":"2026-04-10T10:20:16Z","timestamp":1775816416000},"page":"2345","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["A Narrative Review of AI Frameworks for Chronic Stress Detection Using Physiological Sensing: Resting, Longitudinal, and Reactivity Perspectives"],"prefix":"10.3390","volume":"26","author":[{"ORCID":"https:\/\/orcid.org\/0009-0002-3558-2728","authenticated-orcid":false,"given":"Totok","family":"Nugroho","sequence":"first","affiliation":[{"name":"Department of Information and Communications Engineering, Institute of Science Tokyo, Yokohama 226-8501, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6902-5455","authenticated-orcid":false,"given":"Wahyu","family":"Rahmaniar","sequence":"additional","affiliation":[{"name":"Institute of Integrated Research, Institute of Science Tokyo, Yokohama 226-5801, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3482-971X","authenticated-orcid":false,"given":"Alfian","family":"Ma\u2019arif","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering, Universitas Ahmad Dahlan, Yogyakarta 55191, Indonesia"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,4,10]]},"reference":[{"key":"ref_1","unstructured":"World Health Organization (2026, February 13). Over a Billion People Living with Mental Health Conditions\u2014Services Require Urgent Scale-Up. Available online: https:\/\/www.who.int\/news\/item\/02-09-2025-over-a-billion-people-living-with-mental-health-conditions-services-require-urgent-scale-up."},{"key":"ref_2","unstructured":"OECD (2026, February 13). Mental Health. Available online: https:\/\/www.oecd.org\/en\/topics\/mental-health.html."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"171","DOI":"10.1056\/NEJM199801153380307","article-title":"Protective and Damaging Effects of Stress Mediators","volume":"338","author":"McEwen","year":"1998","journal-title":"N. Engl. J. Med."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"2093","DOI":"10.1001\/archinte.1993.00410180039004","article-title":"Stress and the Individual: Mechanisms Leading to Disease","volume":"153","author":"McEwen","year":"1993","journal-title":"Arch. Intern. Med."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"108","DOI":"10.1016\/S0893-133X(99)00129-3","article-title":"Allostasis and Allostatic Load: Implications for Neuropsychopharmacology","volume":"22","author":"McEwen","year":"2000","journal-title":"Neuropsychopharmacology"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"4770","DOI":"10.1073\/pnas.081072698","article-title":"Allostatic Load as a Marker of Cumulative Biological Risk: MacArthur Studies of Successful Aging","volume":"98","author":"Seeman","year":"2001","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"385","DOI":"10.2307\/2136404","article-title":"A Global Measure of Perceived Stress","volume":"24","author":"Cohen","year":"1983","journal-title":"J. Health Soc. Behav."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"8","DOI":"10.1026\/\/0012-1924.45.1.8","article-title":"Trierer Inventar Zur Erfassung von Chronischem Stre\u00df (TICS): Skalenkonstruktion, Teststatistische \u00dcberpr\u00fcfung Und Validierung Der Skala Arbeits\u00fcberlastung","volume":"45","author":"Schulz","year":"1999","journal-title":"Diagnostica"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"313","DOI":"10.1016\/0306-4530(94)90013-2","article-title":"Salivary Cortisol in Psychoneuroendocrine Research: Recent Developments and Applications","volume":"19","author":"Kirschbaum","year":"1994","journal-title":"Psychoneuroendocrinology"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"32","DOI":"10.1016\/j.psyneuen.2008.08.024","article-title":"Hair as a Retrospective Calendar of Cortisol Production-Increased Cortisol Incorporation into Hair in the Third Trimester of Pregnancy","volume":"34","author":"Kirschbaum","year":"2009","journal-title":"Psychoneuroendocrinology"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1019","DOI":"10.1016\/j.bbi.2012.02.002","article-title":"Analysis of Cortisol in Hair\u2014State of the Art and Future Directions","volume":"26","author":"Stalder","year":"2012","journal-title":"Brain Behav. Immun."},{"key":"ref_12","unstructured":"Squire, L.R. (2009). Heart Rate Variability: A Neurovisceral Integration Model. Encyclopedia of Neuroscience, Academic Press."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"235","DOI":"10.30773\/pi.2017.08.17","article-title":"Stress and Heart Rate Variability: A Meta-Analysis and Review of the Literature","volume":"15","author":"Kim","year":"2018","journal-title":"Psychiatry Investig."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Katmah, R., Al-Shargie, F., Tariq, U., Babiloni, F., Al-Mughairbi, F., and Al-Nashash, H. (2021). A Review on Mental Stress Assessment Methods Using EEG Signals. Sensors, 21.","DOI":"10.20944\/preprints202107.0255.v1"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Luijcks, R., Vossen, C.J., Hermens, H.J., van Os, J., and Lousberg, R. (2015). The Influence of Perceived Stress on Cortical Reactivity: A Proof-Of-Principle Study. PLoS ONE, 10.","DOI":"10.1371\/journal.pone.0129220"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"020","DOI":"10.31083\/j.jin2101020","article-title":"Frontal Lobe Real-Time EEG Analysis Using Machine Learning Techniques for Mental Stress Detection","volume":"21","author":"AlShorman","year":"2022","journal-title":"J. Integr. Neurosci."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Vaishali, B., Amalan, S., Preejith, S.P., Joseph, J., and Sivaprakasam, M. (2020). HRV Based Stress Assessment of Individuals in a Work Environment. Proceedings of the 2020 IEEE International Symposium on Medical Measurements and Applications (MeMeA), Bari, Italy, 1 June\u20131 July 2020, IEEE.","DOI":"10.1109\/MeMeA49120.2020.9137299"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Hosseini, E., Fang, R., Zhang, R., Parenteau, A., Hang, S., Rafatirad, S., Hostinar, C., Orooji, M., and Homayoun, H. (2022). A Low Cost EDA-Based Stress Detection Using Machine Learning. Proceedings of the 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Las Vegas, NV, USA, 6\u20138 December 2022, IEEE.","DOI":"10.1109\/BIBM55620.2022.9995093"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"156","DOI":"10.1109\/TITS.2005.848368","article-title":"Detecting Stress During Real-World Driving Tasks Using Physiological Sensors","volume":"6","author":"Healey","year":"2005","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"259","DOI":"10.1146\/annurev.physiol.67.040403.120816","article-title":"Endocrinology of the Stress Response","volume":"67","author":"Charmandari","year":"2005","journal-title":"Annu. Rev. Physiol."},{"key":"ref_21","first-page":"55","article-title":"How Do Glucocorticoids Influence Stress Responses? Integrating Permissive, Suppressive, Stimulatory, and Preparative Actions","volume":"21","author":"Sapolsky","year":"2000","journal-title":"Endocr. Rev."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Albernaz-Mariano, K.A., Juliano, V.A.L., Duque, E.A., Pereira, R.M., Novaes, L.S., and Munhoz, C.D. (2025). Modeling Stress-Related Hormones and Delayed Anxiety. Handbook of the Biology and Pathology of Mental Disorders, Springer.","DOI":"10.1007\/978-3-031-73368-0_11"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"25","DOI":"10.1037\/0033-2909.133.1.25","article-title":"If It Goes up, Must It Come down? Chronic Stress and the Hypothalamic-Pituitary-Adrenocortical Axis in Humans","volume":"133","author":"Miller","year":"2007","journal-title":"Psychol. Bull."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"434","DOI":"10.1038\/nrn2639","article-title":"Effects of Stress throughout the Lifespan on the Brain, Behaviour and Cognition","volume":"10","author":"Lupien","year":"2009","journal-title":"Nat. Rev. Neurosci."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"1985","DOI":"10.1016\/S0277-9536(03)00402-7","article-title":"Cumulative Biological Risk and Socio-Economic Differences in Mortality: MacArthur Studies of Successful Aging","volume":"58","author":"Seeman","year":"2004","journal-title":"Soc. Sci. Med."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"7870","DOI":"10.1523\/JNEUROSCI.1184-06.2006","article-title":"Stress-Induced Alterations in Prefrontal Cortical Dendritic Morphology Predict Selective Impairments in Perceptual Attentional Set-Shifting","volume":"26","author":"Liston","year":"2006","journal-title":"J. Neurosci."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"201","DOI":"10.1016\/S0165-0327(00)00338-4","article-title":"A Model of Neurovisceral Integration in Emotion Regulation and Dysregulation","volume":"61","author":"Thayer","year":"2000","journal-title":"J. Affect. Disord."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"122","DOI":"10.1016\/j.ijcard.2009.09.543","article-title":"The Relationship of Autonomic Imbalance, Heart Rate Variability and Cardiovascular Disease Risk Factors","volume":"141","author":"Thayer","year":"2010","journal-title":"Int. J. Cardiol."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Khosrowabadi, R., Quek, C., Ang, K.K., Tung, S.W., and Heijnen, M. (2011). A Brain-Computer Interface for Classifying EEG Correlates of Chronic Mental Stress. Proceedings of the 2011 International Joint Conference on Neural Networks, San Jose, CA, USA, 31 July\u20135 August 2011, IEEE.","DOI":"10.1109\/IJCNN.2011.6033297"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"202","DOI":"10.1109\/OJCS.2025.3647369","article-title":"A Multimodal Perceived Stress Classification Framework Using Wearable Physiological Sensors","volume":"7","author":"Majid","year":"2026","journal-title":"IEEE Open J. Comput. Soc."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Sharma, M., Mishra, V., Varun, K., and Panjala, D. (2024). A Two-Stage Anomaly Filter Model for Continuous Stress Monitoring-Based on Ppg. Proceedings of the 2024 8th International Conference on Computational System and Information Technology for Sustainable Solutions (CSITSS), Bengaluru, India, 7\u20139 November 2024, IEEE.","DOI":"10.1109\/CSITSS64042.2024.10816950"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Hag, A., Handayani, D., Pillai, T., Mantoro, T., Kit, M.H., and Al-Shargie, F. (2021). A Wearable Single EEG Channel Analysis for Mental Stress State Detection. Proceedings of the 2021 IEEE 7th International Conference on Computing, Engineering and Design (ICCED), Sukabumi, Indonesia, 5\u20136 August 2021, IEEE.","DOI":"10.1109\/ICCED53389.2021.9664880"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Nakashima, Y., Umematsu, T., Tsujikawa, M., and Onishi, Y. (2019). An Effectiveness Comparison between the Use of Activity State Data and That of Activity Magnitude Data in Chronic Stress Recognition. Proceedings of the 2019 8th International Conference on Affective Computing and Intelligent Interaction Workshops and Demos (ACIIW), Cambridge, UK, 3\u20136 September 2019, IEEE.","DOI":"10.1109\/ACIIW.2019.8925222"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"115400","DOI":"10.1109\/ACCESS.2024.3437671","article-title":"Analysis of Acute Stress Reactivity and Recovery in Autonomic Nervous System Considering Individual Characteristics of Stress Using HRV and EDA","volume":"12","author":"Lee","year":"2024","journal-title":"IEEE Access"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"1178","DOI":"10.1109\/TAFFC.2023.3326747","article-title":"Chronic Stress Recognition Based on Time-Slot Analysis of Ambulatory Electrocardiogram and Tri-Axial Acceleration","volume":"15","author":"Li","year":"2024","journal-title":"IEEE Trans. Affect. Comput."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Arsalan, A., Majid, M., Anwar, S.M., and Bagci, U. (2019). Classification of Perceived Human Stress Using Physiological Signals. Proceedings of the 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin, Germany, 23\u201327 July 2019, IEEE.","DOI":"10.1109\/EMBC.2019.8856377"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"2257","DOI":"10.1109\/JBHI.2019.2926407","article-title":"Classification of Perceived Mental Stress Using A Commercially Available EEG Headband","volume":"23","author":"Arsalan","year":"2019","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Liu, J., He, L., Chen, Z., Chen, Z., Hao, Y., and Jiang, D. (2023). Context-Aware EEG-Based Perceived Stress Recognition Based on Emotion Transition Paradigm. Proceedings of the 2023 11th International Conference on Affective Computing and Intelligent Interaction Workshops and Demos (ACIIW), Cambridge, MA, USA, 10\u201313 September 2023, IEEE.","DOI":"10.1109\/ACIIW59127.2023.10388140"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Saeed, S.M.U., Anwar, S.M., Khalid, H., Majid, M., and Bagci, U. (2020). EEG Based Classification of Long-Term Stress Using Psychological Labeling. Sensors, 20.","DOI":"10.3390\/s20071886"},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Ky, R., Hyodo, M., Segura, M., Mamdani, F., Arangott, R., Garcia, A.K., Cano, M.R., and Gago-Masague, S. (2025). Mobile Computing Framework for Machine Learning-Based Stress Detection. Proceedings of the 2025 IEEE Intelligent Mobile Computing (MobileCloud), Tucson, AZ, USA, 21\u201324 July 2025, IEEE.","DOI":"10.1109\/MobileCloud66020.2025.00009"},{"key":"ref_41","unstructured":"Kuzhaloli, S. (2025). Wearable Tech for Mental Health Monitoring Real-Time Stress Detection Using Biometric Sensors. Proceedings of the 2025 Global Conference in Emerging Technology (GINOTECH), Pune, India, 9\u201311 May 2025, IEEE."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Do, T.T., Van Tran, L., Le, T.A., Thi Le, T.M., Duong, L.-A.H., Nguyen, T.H., Phan, D.T., Van Vo, T., and Thi Ha, H.T. (2023). Stress Prediction Using Machine-Learning Techniques on Physiological Signals. Proceedings of the 2023 1st International Conference on Health Science and Technology (ICHST), Hanoi, Vietnam, 28\u201329 December 2023, IEEE.","DOI":"10.1109\/ICHST59286.2023.10565314"},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Dai, A., and Cui, J. (2025). Robust Chronic Stress Detection on Consumer-Grade Wearable Data: A Two-Stage Learning Framework. Proceedings of the 2025 IEEE International Conference on E-Health Networking, Application & Services (Healthcom), Abu Dhabi, United Arab Emirates, 21\u201323 October 2025, IEEE.","DOI":"10.1109\/HealthCom60686.2025.11342852"},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Tan, H., Wen, W., Xu, H., Liu, L., and Qin, F. (2025). Recognition of Suboptimal Health Status Based on Electrocardiogram and Three-Dimensional Acceleration. Proceedings of the 2025 IEEE 6th International Conference on Pattern Recognition and Machine Learning (PRML), Chongqing, China, 13\u201315 June 2025, IEEE.","DOI":"10.1109\/PRML66062.2025.11159837"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"90","DOI":"10.1037\/a0038100","article-title":"Psychometric Analysis of the Ten-Item Perceived Stress Scale","volume":"27","author":"Taylor","year":"2015","journal-title":"Psychol. Assess."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"24705470221100987","DOI":"10.1177\/24705470221100987","article-title":"Predicting Chronic Stress among Healthy Females Using Daily-Life Physiological and Lifestyle Features from Wearable Sensors","volume":"6","author":"Magal","year":"2022","journal-title":"Chronic Stress"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"1322","DOI":"10.1002\/jclp.21908","article-title":"The Depression Anxiety Stress Scales-21 (DASS-21): Further Examination of Dimensions, Scale Reliability, and Correlates","volume":"68","author":"Osman","year":"2012","journal-title":"J. Clin. Psychol."},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Lovibond, S.H., and Lovibond, P.F. (1996). Manual for the Depression Anxiety Stress Scales, Psychology Foundation of Australia.","DOI":"10.1037\/t01004-000"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"176","DOI":"10.1037\/1040-3590.10.2.176","article-title":"Psychometric Properties of the 42-Item and 21-Item Versions of the Depression Anxiety Stress Scales in Clinical Groups and a Community Sample","volume":"10","author":"Antony","year":"1998","journal-title":"Psychol. Assess."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"227","DOI":"10.1348\/014466505X29657","article-title":"The Short-Form Version of the Depression Anxiety Stress Scales (DASS-21): Construct Validity and Normative Data in a Large Non-Clinical Sample","volume":"44","author":"Henry","year":"2005","journal-title":"Br. J. Clin. Psychol."},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Afek, N., Harmatiuk, D., Gaw\u0142owska, M., Ferreira, J.M.A., Golonka, K., Tukaiev, S., Popov, A., and Marek, T. (2025). Functional Connectivity in Burnout Syndrome: A Resting-State EEG Study. Front. Hum. Neurosci., 19.","DOI":"10.3389\/fnhum.2025.1481760"},{"key":"ref_52","first-page":"1","article-title":"Detection of Chronic Stress Using Bio-Signals","volume":"9","year":"2016","journal-title":"Indian J. Sci. Technol."},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Kim, D., Seo, Y., Cho, J., and Cho, C.-H. (2008). Detection of Subjects with Higher Self-Reporting Stress Scores Using Heart Rate Variability Patterns during the Day. Proceedings of the 2008 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Vancouver, BC, Canada, 20\u201325 August 2008, IEEE.","DOI":"10.1109\/IEMBS.2008.4649244"},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"e3493","DOI":"10.1002\/smi.3493","article-title":"Chronic Stress and Hair Cortisol Concentration in Mothers: A Two-Study Investigation","volume":"40","author":"Rinne","year":"2024","journal-title":"Stress Health"},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Lynch, R., Flores-Torres, M.H., Hinojosa, G., Aspelund, T., Hauksd\u00f3ttir, A., Kirschbaum, C., Catzin-Kuhlmann, A., Lajous, M., and Valdimarsdottir, U. (2022). Perceived Stress and Hair Cortisol Concentration in a Study of Mexican and Icelandic Women. PLoS Glob. Public Health, 2.","DOI":"10.1371\/journal.pgph.0000571"},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"199","DOI":"10.1080\/10253890601029751","article-title":"Examination Stress as an Ecological Inducer of Cortisol and Psychological Responses to Stress in Undergraduate Students","volume":"9","author":"Weekes","year":"2006","journal-title":"Stress"},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"640","DOI":"10.1093\/eurheartj\/ehm584","article-title":"Work Stress and Coronary Heart Disease: What Are the Mechanisms?","volume":"29","author":"Chandola","year":"2008","journal-title":"Eur. Heart J."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"439","DOI":"10.1016\/j.neuron.2016.05.038","article-title":"Dysregulation of Prefrontal Cortex-Mediated Slow-Evolving Limbic Dynamics Drives Stress-Induced Emotional Pathology","volume":"91","author":"Hultman","year":"2016","journal-title":"Neuron"},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"880","DOI":"10.1161\/01.HYP.35.4.880","article-title":"Effects of Work Stress on Ambulatory Blood Pressure, Heart Rate, and Heart Rate Variability","volume":"35","author":"Vrijkotte","year":"2000","journal-title":"Hypertension"},{"key":"ref_60","doi-asserted-by":"crossref","unstructured":"Liao, J., Brunner, E.J., and Kumari, M. (2013). Is There an Association between Work Stress and Diurnal Cortisol Patterns? Findings from the Whitehall II Study. PLoS ONE, 8.","DOI":"10.1371\/journal.pone.0081020"},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"177","DOI":"10.1111\/j.1468-5884.2011.00464.x","article-title":"Blunted as Well as Exaggerated Cardiovascular Reactivity to Stress Is Associated with Negative Health Outcomes","volume":"53","author":"Phillips","year":"2011","journal-title":"Jpn. Psychol. Res."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"2273","DOI":"10.1007\/s11517-018-1854-8","article-title":"Objective Detection of Chronic Stress Using Physiological Parameters","volume":"56","author":"Alhitary","year":"2018","journal-title":"Med. Biol. Eng. Comput."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"234","DOI":"10.1016\/j.biopsych.2007.04.041","article-title":"Deactivation of the Limbic System during Acute Psychosocial Stress: Evidence from Positron Emission Tomography and Functional Magnetic Resonance Imaging Studies","volume":"63","author":"Pruessner","year":"2008","journal-title":"Biol. Psychiatry"},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1677\/JOE-08-0415","article-title":"Glucocorticoids and the Circadian Clock","volume":"200","author":"Dickmeis","year":"2009","journal-title":"J. Endocrinol."},{"key":"ref_65","unstructured":"Lumauig, G. (2026, March 10). Understanding Electroencephalogram (EEG) for Better Brain Health. Available online: https:\/\/www.neeuro.com\/blog\/understanding-electroencephalogram-eeg-for-better-brain-health."},{"key":"ref_66","doi-asserted-by":"crossref","unstructured":"Nugroho, T., Yoshimura, N., and Tejada, L.A.M. (2024). Detection of Chronic Stress Based on Electroencephalography Responses Induced by the Stroop Color Word Test. Proceedings of the 2024 16th Biomedical Engineering International Conference (BMEiCON), Chon Buri, Thailand, 21\u201323 November 2024, IEEE.","DOI":"10.1109\/BMEiCON64021.2024.10896292"},{"key":"ref_67","doi-asserted-by":"crossref","unstructured":"Li, K., Cardoso, C., Moctezuma-Ramirez, A., Elgalad, A., and Perin, E. (2023). Heart Rate Variability Measurement through a Smart Wearable Device: Another Breakthrough for Personal Health Monitoring?. Int. J. Environ. Res. Public Health, 20.","DOI":"10.20944\/preprints202308.0732.v1"},{"key":"ref_68","doi-asserted-by":"crossref","unstructured":"Gwak, M., Davis, T., Sarrafzadeh, M., and Woo, E. (2021). Psychological Stress Detection in Older Adults with Cognitive Impairment Using Photoplethysmography. Proceedings of the 2021 IEEE 9th International Conference on Healthcare Informatics (ICHI), Victoria, BC, Canada, 9\u201312 August 2021, IEEE.","DOI":"10.1109\/ICHI52183.2021.00040"},{"key":"ref_69","doi-asserted-by":"crossref","unstructured":"Maharani, S., Rokhana, R., and Puspita, E. (2023). Stress Condition Detector Based on the Physiological Response Using K-Means Cluster Analysis. Proceedings of the 2023 International Electronics Symposium (IES), Denpasar, Indonesia, 8\u201310 August 2023, IEEE.","DOI":"10.1109\/IES59143.2023.10242493"},{"key":"ref_70","doi-asserted-by":"crossref","unstructured":"Schmidt, P., Reiss, A., Duerichen, R., Marberger, C., and Van Laerhoven, K. (2018). Introducing WESAD, a Multimodal Dataset for Wearable Stress and Affect Detection. Proceedings of the 20th ACM International Conference on Multimodal Interaction, Boulder, CO, USA, 16\u201320 October 2018, ACM.","DOI":"10.1145\/3242969.3242985"},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"3095","DOI":"10.1109\/TNSRE.2024.3447274","article-title":"Accurate Mental Stress Detection Using Sequential Backward Selection and Adaptive Synthetic Methods","volume":"32","author":"Tseng","year":"2024","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"333","DOI":"10.1049\/wss2.12083","article-title":"LSTM-Based Real-Time Stress Detection Using PPG Signals on Raspberry Pi","volume":"14","author":"Rostami","year":"2024","journal-title":"IET Wirel. Sens. Syst."},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"6959","DOI":"10.1007\/s13369-024-09095-2","article-title":"Real-Time Stress Detection from Raw Noisy PPG Signals Using LSTM Model Leveraging TinyML","volume":"50","author":"Rostami","year":"2025","journal-title":"Arab. J. Sci. Eng."},{"key":"ref_74","doi-asserted-by":"crossref","unstructured":"Tahira, M., and Vyas, P. (2023). EEG Based Mental Stress Detection Using Deep Learning Techniques. Proceedings of the 2023 International Conference on Distributed Computing and Electrical Circuits and Electronics (ICDCECE), Ballari, India, 28\u201329 April 2023, IEEE.","DOI":"10.1109\/ICDCECE57866.2023.10150574"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/26\/8\/2345\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,10]],"date-time":"2026-04-10T10:21:49Z","timestamp":1775816509000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/26\/8\/2345"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,4,10]]},"references-count":74,"journal-issue":{"issue":"8","published-online":{"date-parts":[[2026,4]]}},"alternative-id":["s26082345"],"URL":"https:\/\/doi.org\/10.3390\/s26082345","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,4,10]]}}}