Stress affect detection using wesad data: a machine learning approach to mental health monitoring

Venkatesh Maheshwaram; Venkatesh Artham; Subburu Mamatha

Author(s)

Venkatesh Maheshwaram Department of Electronics and Communication Engineering Sree Dattha Group of Institutions, Sheriguda, Hyderabad, Telangana

Venkatesh Artham Sree Dattha Group of Institutions,Sheriguda, Hyderabad, Telangana,

Subburu Mamatha Sree Dattha Group of Institutions,Sheriguda, Hyderabad, Telangana,

Abstract

Stress has become a common issue influencing people in many different spheres in the fast-paced environment of today. Maintaining both physical and psychological health depends on the ability to recognize and control stress. The increased knowledge of the influence of stress on health and productivity leads to the necessity of automated stress impacts detection. Conventional methods of stress evaluation can depend on subjective and time-consuming self-reported questionnaires or clinical assessments. Therefore, it is feasible to give people quick and accurate feedback by using machine learning models, therefore empowering them to take proactive actions to control their stress level. Developing machine learning models that can efficiently interpret physiological data from wearable sensors (like those given by the WESAD dataset) to precisely detect and categorize stress affect states is the main difficulty of this work. This entails building strong features from the unprocessed sensor data and training models able to generalize among several people and situations. Although physiological measurements have been applied, integration of machine learning marks a major progress above traditional statistical methods. This use of machine learning for stress impact identification marks a paradigm change in our approach of stress management. This suggested system offers people immediate insights on their stress levels by using wearable sensor data and sophisticated algorithms, therefore enabling them to take proactive action towards improved mental and physical well-being.

Publication Details

Page(s) 1961-1969

DOI DOI not available

Published Journal: International Journal of Communication Networks and Information Security, Volume: 16, Issue: 4, Year: 2024

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Keywords

demographics., stress affect, wesad data

References

Li .,C. . .2021 .From Involution to Education: A Glance to Chinese Young Generation. In Proceedings of the 2021 4th International Conference on Humanities Education and Social Sciences (ICHESS, : 1884-1887.

Ponzini .,A. Educating . .2020 .the new Chinese middle-class youth: The role of quality education on ideas of class and status. J. Chin. Sociol, 7 : .

Hetrick M.C.,Parker S.E.,A.G. S.E. .2020 .The impact of stress on students in secondary school and higher education. Int. J. Adolesc. Youth, 25 : 104-112.

Solutions Potential .2023 .. (accessed on 19 February, : .

Wagh .,Vasanth K.P.,K. K.P. .2022 .Performance evaluation of multi-channel electroencephalogram signal (EEG) based time frequency analysis for human emotion recognition. Biomed. Signal Process. Control, 78 : 103966.

S. ,R. ,Murray P.,N. P. . .CNN-based Emotion Recognition from Multimodal Peripheral Physiological Signals. In Proceedings of the IMX'22: ACM International Conference on Interactive Media Experiences, : 22-24.

Zheng M.,Xu L.,Yang B.,Hu Z.,W. Z. .2023 .A multiple frequency bands parallel spatialtemporal 3D deep residual learning framework for EEG-based emotion recognition. Biomed. Signal Process. Control, 79 : 104141.

Quispe K.G.,Utyiama D.M.,Santos Dos,Oliveira E.M.,Souto H.A.,Applying E.J. .2022 .Self-Supervised Representation Learning for Emotion Recognition Using Physiological Signals. Sensors, 22 : 9102.

Tang .,Wang Y.,Zhang Y.,Wang X.,Z. X. .2022 .STILN: A Novel Spatial-Temporal Information Learning Network for EEG-based Emotion Recognition. arXiv, : 12103.

Lee J.S.,Hwang M.,G. M.,Lee G.,S.J. G. .2022 .Attention-LRCN: Long-term Recurrent Convolutional Network for Stress Detection from Photoplethysmography. In Proceedings of the 2022 IEEE International Symposium on Medical Measurements and Applications, : 1-6.

X. .2022 .Wenjuanxing Official Website. (accessed on 19 December, : .

.2022 .. (accessed on 21 December, : .

.2023 .BMD101 ECG Detection Package. (accessed on 19 June, : .

.2022 .. (accessed on 8 August, : .

S. ,Jitpattanakul N.,A. N.,Technology (ECTI-CON ) .2022 .A Deep Residual-based Model on MultiBranch Aggregation for Stress and Emotion Recognition through Biosignals. Prachuap Khiri Khan, Thailand, : 1-4.

Lucas H.I.,Forestier B.,G. B.,Schmidt C.,Weber D.F.,J. D.F.,Idoumghar G.I.,Muller L.,Petitjean P.,F. P. .2020 .Inceptiontime: Finding alexnet for time series classification. Data Min. Knowl. Discov, 34 : 1936-1962.