Trends and Challenges in Real-Time Stress Detection and Modulation: The Role of the IoT and Artificial Intelligence

Manuel Paniagua-Gómez; Manuel Fernandez-Carmona

doi:10.3390/electronics14132581

journal article Open Access Jun 26, 2025

Trends and Challenges in Real-Time Stress Detection and Modulation: The Role of the IoT and Artificial Intelligence

Manuel Paniagua-Gómez

Manuel Fernandez-Carmona

Electronics Vol. 14 No. 13 pp. 2581 · MDPI AG

View at Publisher Save 10.3390/electronics14132581

Abstract

The integration of Internet of Things (IoT) devices and Artificial Intelligence (AI) has opened new frontiers in mental health, particularly in stress detection and management. This review explores the current literature, examining how IoT-enabled wearables, sensors, and mobile applications, combined with AI algorithms, are utilized to monitor physiological and behavioral indicators of stress. Advancements in real-time stress detection, personalized interventions, and predictive modeling are highlighted, alongside a critical evaluation of existing technologies. While significant progress has been made in the field, several limitations persist, including challenges with the accuracy of stress detection, the scalability of solutions, and the generalizability of AI models across diverse populations. Key challenges are further analyzed, such as ensuring data privacy and security, achieving seamless technological integration, and advancing model personalization to account for individual variability in stress responses. Addressing these challenges is essential to developing robust, ethical, and user-centric solutions that can transform stress management in mental healthcare. This review concludes with recommendations for future research directions aimed at overcoming current barriers and enhancing the effectiveness of IoT- and AI-driven approaches to stress management.

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References

59

[1]

Rau, P.L. A Framework of Real-Time Stress Monitoring and Intervention System. Proceedings of the Cross-Cultural Design. Applications in Health, Learning, Communication, and Creativity.

[2]

(2025, May 03). IoT in Wearables 2025: Devices, Examples and Industry Overview. Available online: https://stormotion.io/blog/iot-in-wearables/.

[3]

Abd Al-Alim, M., Mubarak, R., Salem, N.M., and Sadek, I. (2024). A Machine-Learning Approach for Stress Detection Using Wearable Sensors in Free-Living Environments. Comput. Biol. Med., 179. 10.1016/j.compbiomed.2024.108918

[4]

Mohamad Jawad, H., Bin Hassan, Z., Zaidan, B., Mohammed Jawad, F., Mohamed Jawad, D., and Alredany, W. (2022). A Systematic Literature Review of Enabling IoT in Healthcare: Motivations, Challenges, and Recommendations. Electronics, 11. 10.3390/electronics11193223

[5]

Mattioli, V., Davoli, L., Belli, L., Gambetta, S., Carnevali, L., Sgoifo, A., Raheli, R., and Ferrari, G. (2024). IoT-Based Assessment of a Driver’s Stress Level. Sensors, 24. 10.3390/s24175479

[6]

Muñoz Arteaga, J., and Hernádez, Y. (2014). Temas de Diseño En Interacción Humano-Computadora, Iniciativa Latinoamericana de Libros de Texto Abiertos (LATIn).

[7]

Biggs, A., Brough, P., and Drummond, S. (2017). Lazarus and Folkman’s Psychological Stress and Coping Theory. The Handbook of Stress and Health, John Wiley & Sons, Ltd. 10.1002/9781118993811.ch21

[8]

Pei "Affective Computing: Recent Advances, Challenges, and Future Trends" Intell. Comput. (2024) 10.34133/icomputing.0076

[9]

(2025, May 03). User Experience: UX in IoT: Designing for a Connected Experience. Available online: https://fastercapital.com/content/User-experience--UX---UX-in-IoT--UX-in-IoT--Designing-for-a-Connected-Experience.html.

[10]

Cai, Y., and Abascal, J. (2006). Empathic Computing. Ambient Intelligence in Everyday Life: Foreword by Emile Aarts, Springer. 10.1007/11825890

[11]

Roshanaei, M., Rezapour, R., and El-Nasr, M. (2024). Talk, Listen, Connect: Navigating Empathy in Human-AI Interactions. arXiv.

[12]

Lazarou "Predicting Stress Levels Using Physiological Data: Real-Time Stress Prediction Models Utilizing Wearable Devices" AIMS Neurosci. (2024) 10.3934/neuroscience.2024006

[13]

Vos "Generalizable Machine Learning for Stress Monitoring from Wearable Devices: A Systematic Literature Review" Int. J. Med. Inform. (2023) 10.1016/j.ijmedinf.2023.105026

[14]

Mozos "Stress Detection Using Wearable Physiological and Sociometric Sensors" Int. J. Neural Syst. (2017) 10.1142/s0129065716500416

[15]

Schmidt, P., Reiss, A., Duerichen, R., Marberger, C., and Van Laerhoven, K. (2018, January 16–20). Introducing WESAD, a Multimodal Dataset for Wearable Stress and Affect Detection. Proceedings of the 20th ACM International Conference on Multimodal Interaction, ICMI ’18, New York, NY, USA. 10.1145/3242969.3242985

[16]

Luna-Perejón, F., Montes-Sánchez, J., Durán-López, L., Vazquez-Baeza, A., Beasley-Bohórquez, I., and Sevillano-Ramos, J. (2021). IoT Device for Sitting Posture Classification Using Artificial Neural Networks. Electronics, 10. 10.3390/electronics10151825

[17]

Androutsou, T., Angelopoulos, S., Hristoforou, E., Matsopoulos, G., and Koutsouris, D. (2023). A Multisensor System Embedded in a Computer Mouse for Occupational Stress Detection. Biosensors, 13. 10.3390/bios13010010

[18]

Talaat "Stress Monitoring Using Wearable Sensors: IoT Techniques in Medical Field" Neural Comput. Appl. (2023) 10.1007/s00521-023-08681-z

[19]

Bolpagni, M., Pardini, S., Dianti, M., and Gabrielli, S. (2024). Personalized Stress Detection Using Biosignals from Wearables: A Scoping Review. Sensors, 24. 10.20944/preprints202404.1829.v1

[20]

Joseph, J., and Judy, M. (2024, January 19–20). Dynamic Emotion Regulation through Reinforcement Learning: A Neurofeedback System with EEG Data and Custom Wearable Interventions. Proceedings of the 2024 International Conference on Brain Computer Interface & Healthcare Technologies (iCon-BCIHT), IEEE, Thiruvananthapuram, India. 10.1109/icon-bciht63907.2024.10882363

[21]

Patil "Optimized EEG-Based Stress Detection: A Novel Approach" Biomed. Pharmacol. J. (2024) 10.13005/bpj/3052

[22]

From Neural Networks to Emotional Networks: A Systematic Review of EEG-Based Emotion Recognition in Cognitive Neuroscience and Real-World Applications

Evgenia Gkintoni, Anthimos Aroutzidis, Hera Antonopoulou et al.

Brain Sciences 10.3390/brainsci15030220

[23]

Ballesteros, J.A., Ramírez V, G.M., Moreira, F., Solano, A., and Pelaez, C.A. (2024). Facial Emotion Recognition through Artificial Intelligence. Front. Comput. Sci., 6. 10.3389/fcomp.2024.1359471

[24]

Ma "Facial Expression Recognition with Visual Transformers and Attentional Selective Fusion" IEEE Trans. Affect. Comput. (2023) 10.1109/taffc.2021.3122146

[25]

Hindu, A., and Bhowmik, B. (2022, January 24–26). An IoT-Enabled Stress Detection Scheme Using Facial Expression. Proceedings of the 2022 IEEE 19th India Council International Conference (INDICON), Kerala, India. 10.1109/indicon56171.2022.10040216

[26]

(2025, May 03). WESAD (Wearable Stress and Affect Detection) Dataset. Available online: https://www.kaggle.com/datasets/orvile/wesad-wearable-stress-affect-detection-dataset.

[27]

(2025, May 03). AMIGOS: A Dataset for Affect, Personality and Mood Research on Individuals and Groups. Available online: http://www.eecs.qmul.ac.uk/mmv/datasets/amigos/.

[28]

Li, H., Sun, G., Li, Y., and Yang, R. (2021). Wearable Wireless Physiological Monitoring System Based on Multi-Sensor. Electronics, 10. 10.3390/electronics10090986

[29]

Nancy, A., Ravindran, D., Raj Vincent, P., Srinivasan, K., and Gutierrez Reina, D. (2022). IoT-Cloud-Based Smart Healthcare Monitoring System for Heart Disease Prediction via Deep Learning. Electronics, 11. 10.3390/electronics11152292

[30]

Al-Atawi, A., Alyahyan, S., Alatawi, M., Sadad, T., Manzoor, T., Farooq-i-Azam, M., and Khan, Z. (2023). Stress Monitoring Using Machine Learning, IoT and Wearable Sensors. Sensors, 23. 10.3390/s23218875

[31]

Razavi, M., Ziyadidegan, S., Jahromi, R., Kazeminasab, S., Janfaza, V., Mahmoudzadeh, A., Baharlouei, E., and Sasangohar, F. (2024). Machine Learning, Deep Learning and Data Preprocessing Techniques for Detection, Prediction, and Monitoring of Stress and Stress-related Mental Disorders: A Scoping Review. arXiv. 10.2196/preprints.53714

[32]

Moshawrab, M., Adda, M., Bouzouane, A., Ibrahim, H., and Raad, A. (2023). Reviewing Multimodal Machine Learning and Its Use in Cardiovascular Diseases Detection. Electronics, 12. 10.3390/electronics12071558

[33]

Marković, D., Vujičić, D., Stojić, D., Jovanović, Ž., Pešović, U., and Ranđić, S. (2019, January 20–22). Monitoring System Based on IoT Sensor Data with Complex Event Processing and Artificial Neural Networks for Patients Stress Detection. Proceedings of the 2019 18th International Symposium INFOTEH-JAHORINA (INFOTEH), Jahorina, Bosnia and Herzegovina. 10.1109/infoteh.2019.8717748

[34]

Johnson "Adaptive Just-in-Time Intervention to Reduce Everyday Stress Responses: Protocol for a Randomized Controlled Trial" JMIR Res. Protoc. (2025) 10.2196/58985

[35]

Liu, M., Ren, S., Ma, S., Jiao, J., Chen, Y., Wang, Z., and Song, W. (2021). Gated transformer networks for multivariate time series classification. arXiv.

[36]

Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecasting

Haoyi Zhou, Shanghang Zhang, Jieqi Peng et al.

Proceedings of the AAAI Conference on Artificial I... 10.1609/aaai.v35i12.17325

[37]

Multimodal Emotion Detection via Attention-Based Fusion of Extracted Facial and Speech Features

Dilnoza Mamieva, Akmalbek Bobomirzaevich Abdusalomov, Alpamis Kutlimuratov et al.

Sensors 10.3390/s23125475

[38]

Sun, F.T., Cynthia, K., Cheng, H.T., Buthpitiya, S., Collins, P., and Griss, M. (2012). Activity-Aware Mental Stress Detection Using Physiological Sensors. Mobile Computing, Applications, and Services—MobiCASE 2010, Springer. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering. 10.1007/978-3-642-29336-8_12

[39]

Irshad "Towards Enhancing Security of IoT-Enabled Healthcare System" Heliyon (2023) 10.1016/j.heliyon.2023.e22336

[40]

Boussada, R., Hamdane, B., Elhdhili, M., and Saidane, L. (2019, January 15–18). PP-NDNoT: On Preserving Privacy in IoT-Based E-Health Systems over NDN. Proceedings of the 2019 IEEE Wireless Communications and Networking Conference (WCNC), Marrakesh, Morocco. 10.1109/wcnc.2019.8886110

[41]

(2025, May 03). The Fusion of Internet of Things, Artificial Intelligence, and Cloud Computing in Health Care. Available online: https://www.springerprofessional.de/the-fusion-of-internet-of-things-artificial-intelligence-and-clo/19562918.

[42]

Mohamed "A Survey of Machine and Deep Learning Methods for Internet of Things (IoT) Security" IEEE Commun. Surv. Tutor. (2020) 10.1109/comst.2020.2988293

[43]

Weber "Internet of Things—New security and privacy challenges" Comput. Law Secur. Rev. (2010) 10.1016/j.clsr.2009.11.008

[44]

Mittelstadt, B. (2017). Designing the Health-Related Internet of Things: Ethical Principles and Guidelines. Information, 8. 10.2139/ssrn.2943006

[45]

Pati "Privacy preservation for federated learning in health care" Patterns (2024) 10.1016/j.patter.2024.100974

[46]

Li "Federated Learning: Challenges, Methods, and Future Directions" IEEE Signal Process. Mag. (2020)

[47]

Abadi, M., Chu, A., Goodfellow, I., McMahan, H.B., Mironov, I., Talwar, K., and Zhang, L. (2016, January 24–28). Deep Learning with Differential Privacy. Proceedings of the 2016 ACM SIGSAC Conference on Computer and Communications Security, Vienna, Austria. 10.1145/2976749.2978318

[48]

Zhang, H., Wu, B., Yang, X., Yuan, X., Liu, X., and Yi, X. (May, January 28). Dynamic Graph Unlearning: A General and Efficient Post-Processing Method via Gradient Transformation. Proceedings of the ACM on Web Conference 2025, Sydney, NSW, Australia. 10.1145/3696410.3714911

[49]

Kumar, V., and Roy, D. (2025). Machine Unlearning Models for Medical Care and Health Data Privacy in Healthcare 6.0. Exploration of Transformative Technologies in Healthcare 6.0 Eds., IGI Global Scientific Publishing. 10.4018/979-8-3693-7210-4.ch010

[50]

Wang, Z.J., Kale, A., Nori, H., Stella, P., Nunnally, M., Chau, D.H., Vorvoreanu, M., Vaughan, J.W., and Caruana, R. (2021). GAM Changer: Editing Generalized Additive Models with Interactive Visualization. arXiv.

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Details

Published: Jun 26, 2025
Vol/Issue: 14(13)
Pages: 2581
License: View

Authors

M

Manuel Paniagua-Gómez

Ingeniería de Sistemas Integrados Group, Department of Electronic Technology, University of Málaga, Bulevar Louis Pasteur, 35, 29071 Málaga, Spain

M

Manuel Fernandez-Carmona

Ingeniería de Sistemas Integrados Group, Department of Electronic Technology, University of Málaga, Bulevar Louis Pasteur, 35, 29071 Málaga, Spain

Funding

Agencia Estatal de Investigación Award: PID2021–127221OB-I00

Cite This Article

Manuel Paniagua-Gómez, Manuel Fernandez-Carmona (2025). Trends and Challenges in Real-Time Stress Detection and Modulation: The Role of the IoT and Artificial Intelligence. Electronics, 14(13), 2581. https://doi.org/10.3390/electronics14132581

Trends and Challenges in Real-Time Stress Detection and Modulation: The Role of the IoT and Artificial Intelligence

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