STRESS CLASSIFICATION USING PHYSIOLOGICAL SIGNALS: A COMPREHENSIVE REVIEW OF METHODS AND APPROACHES COMBINED WITH A NOVEL CNN-BASED ECG EXPERIMENT
DOI:
https://doi.org/10.47820/recima21.v6i9.6745Keywords:
Classification, Stress, Physiological signalsAbstract
Accurate stress detection through physiological signals holds significant promise for improving healthcare outcomes, reducing costs, and enabling early intervention in stress-related disorders. This study presents a comprehensive review of recent advances in stress classification using physiological data, highlighting key methods, challenges, and emerging trends in the field. Particular emphasis is placed on the limitations posed by small datasets, the importance of personalized models, and the difficulties of real-time application in uncontrolled environments. In parallel, we propose and evaluate a novel convolutional neural network (CNN) architecture designed to classify electrocardiogram (ECG) signals into four distinct categories. The model demonstrates robust learning and moderate generalization under data-constrained conditions, achieving 60.95% accuracy on an independent test set. The findings reinforce the efficacy of deep learning in stress classification and underscore the necessity for personalized, real-time, and multimodal approaches in future research.
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BOBADE, P.; VANI, M. Stress detection with machine learning and deep learning using multimodal physiological data. In: INTERNATIONAL CONFERENCE ON INVENTIVE RESEARCH IN COMPUTING APPLICATIONS (ICIRCA), 2., 2020. Proceedings […]. [S. l.]: IEEE, 2020. p. 51–57. DOI: 10.1109/ICIRCA48905.2020.9183244.
CAN, Y. S.; CHALABIANLOO, N.; EKIZ, D.; FERNANDEZ-ALVAREZ, J.; RIVA, G.; ERSOY, C. Personal stress-level clustering and decision-level smoothing to enhance the performance of ambulatory stress detection with smartwatches. IEEE Access, v. 8, p. 38146–38163, 2020.
DELMASTRO, F.; MARTINO, F. D.; DOLCIOTTI, C. Cognitive training and stress detection in MCI frail older people through wearable sensors and machine learning. IEEE Access, v. 8, p. 65573–65590, 2020.
DHAM, V. et al. Mental stress detection using artificial intelligence models. Journal of Physics: Conference Series, v. 1950, 012047, 2021.
GARG, P.; SANTHOSH, J.; DENGEL, A.; ISHIMARU, S. Stress detection by machine learning and wearable sensors. In: INTERNATIONAL CONFERENCE ON INTELLIGENT USER INTERFACES – COMPANION, 26., 2021, New York. Proceedings […]. New York: ACM, 2021. p. 43–45. DOI: 10.1145/3397482.3450732.
GEDAM, S.; PAUL, S. A review on mental stress detection using wearable sensors and machine learning techniques. IEEE Access, v. 9, p. 84045–84066, 2021. DOI: 10.1109/ACCESS.2021.3085502.
GJORESKI, M.; LUŠTREK, M.; GAMS, M.; GJORESKI, H. Monitoring stress with a wrist device using context. Journal of Biomedical Informatics, v. 73, p. 159–170, 2017. DOI: 10.1016/j.jbi.2017.08.006.
HUANG, J.; LIU, Y.; PENG, X. Recognition of driver’s mental workload based on physiological signals: a comparative study. Biomedical Signal Processing and Control, v. 71, parte A, p. 103094, 2022. DOI: 10.1016/j.bspc.2021.103094.
KUMAR, A.; SHARMA, K.; SHARMA, A. Hierarchical deep neural network for mental stress state detection using IoT based biomarkers. Pattern Recognition Letters, v. 145, p. 81–87, 2021. DOI: 10.1016/j.patrec.2021.01.030.
NATH, R. K.; THAPLIYAL, H.; CABAN-HOLT, A. Machine learning based stress monitoring in older adults using wearable sensors and cortisol as stress biomarker. Journal of Signal Processing Systems, 2021. DOI: 10.1007/s11265-020-01611-5.
VARGAS-LOPEZ, O.; PEREZ-RAMIREZ, C. A.; VALTIERRA-RODRIGUEZ, M.; YANEZ-BORJAS, J. J.; AMEZQUITA-SANCHEZ, J. P. An explainable machine learning approach based on statistical indexes and SVM for stress detection in automobile drivers using electromyographic signals. Sensors, v. 21, n. 3155, 2021. DOI: 10.3390/s21093155.
ZUBAIR, M.; YOON, C. Multilevel mental stress detection using ultrashort pulse rate variability series. Biomedical Signal Processing and Control, v. 57, 2020. Art. 101736.
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