CV-SAS: Computer Vision Modeled Seamless Stress Analysis System for Workers

B. Muthu Senthil *, Dinesh E. **, Dinesh T. ***, Goutham Raj S. ****
*-**** Department of Computer Science and Engineering, SRM Valliammai Engineering College, Kattankulathur, Tamil Nadu, India.
Periodicity:July - December'2020
DOI : https://doi.org/10.26634/jpr.7.2.18127

Abstract

People spend most of their time in the workplace, often with high workloads and time pressure, a practice that contributes to increased stress levels. An accurate stress assessment method may thus be of importance to clinical intervention and diseases prevention. While different neuro imaging modalities have been proposed to detect mental stress, each modality experiences certain limitations. This paper proposes the ability of a computer to detect and analyze the stress in human beings with greater accuracy. The system uses the webcam to detect the facial features and perform a mood analysis, which is eventually used for detecting stress. Then, the features are fed into the SVM model as an input and using the machine learning approach the stress is classified.

Keywords

Computer Vision, Mental Stress, Real Time Stress Analysis, Machine Learning, Sentiment Analysis, SVM Model.

How to Cite this Article?

Muthusenthil, B., Dinesh, E., Dinesh, T., and Raj, S. G. (2020). CV-SAS: Computer Vision Modeled Seamless Stress Analysis System for Workers. i-manager's Journal on Pattern Recognition, 7(2), 29-36. https://doi.org/10.26634/jpr.7.2.18127

References

[1]. Arpaia, P., Moccaldi, N., Prevete, R., Sannino, I., & Tedesco, A. (2020). A wearable EEG instrument for realtime frontal asymmetry monitoring in worker stress analysis. IEEE Transactions on Instrumentation and Measurement, 69(10), 8335-8343.
[2]. Bobade, P., & Vani, M. (2020, July). Stress detection with machine learning and deep learning using multimodal physiological data. In 2020, Second International Conference on Inventive Research in Computing Applications (ICIRCA) (pp. 51-57). IEEE.
[3]. Giannakakis, G., Pediaditis, M., Manousos, D., Kazantzaki, E., Chiarugi, F., Simos, P. G., ..., & Tsiknakis, M. (2017). Stress and anxiety detection using facial cues from videos. Biomedical Signal Processing and Control, 31, 89- 101.
[4]. Global Organization for Stress on Stress Facts. (2020). Retrieved from http://www.gostress.com/stress-facts
[5]. Kim, J., & André, E. (2008). Emotion recognition based on physiological changes in music listening. IEEE Transactions on Pattern Analysis and Machine Intelligence, 30(12), 2067-2083.
[6]. Koldijk, S., Neerincx, M. A., & Kraaij, W. (2016). Detecting work stress in offices by combining unobtrusive sensors. IEEE Transactions on Affective Computing, 9(2), 227-239.
[7]. Koldijk, S., Sappelli, M., Verberne, S., Neerincx, M. A., & Kraaij, W. (2014, November). The swell knowledge work dataset for stress and user modeling research. In th Proceedings of the 16 International Conference on Multimodal Interaction (pp. 291-298). https://doi.org/10.1 145/2663204.2663257
[8]. Minguillon, J., Perez, E., Lopez-Gordo, M. A., Pelayo, F., & Sanchez-Carrion, M. J. (2018). Portable system for realtime detection of stress level. Sensors, 18(8), 2504. https://doi.org/10.3390/s18082504
[9]. Padmaja, B., Prasad, V. R., & Sunitha, K. V. N. (2018). A machine learning approach for stress detection using a wireless physical activity tracker. International Journal of Machine Learning and Computing, 8(1), 33-38. https://doi. org/10.18178/ijmlc.2018.8.1.659
[10]. Sarkar, B. (2020, December 7). Seven in 10 Indian workers say they're experiencing stress at work on weekly basis: Report. The Economic Times. Retrieved from https://economictimes.indiatimes.com/news/company/c orporate-trends/seven-in-10-indian-workers-say-theyreexperiencing- stress-at-work-on-weekly-basis-report/articles how/79607886.cms?from=mdr
[11]. Schmidt, P., Reiss, A., Duerichen, R., Marberger, C., & Van Laerhoven, K. (2018, October). Introducing WESAD, a multimodal dataset for wearable stress and affect detection. In Proceedings of the 20th ACM International Conference on Multimodal Interaction (pp. 400-408). https://doi.org/10.1145/3242969.3242985
[12]. Spiers, D. L. (2016). Facial emotion detection using deep learning. (Degree project). Department of Information Technology, Uppsala University, Sweden.
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