Abstract
In today's fast-paced world, stress is common on various occasions in everyday life. However, long-term stress hinders normal lives. Detection of such mental stress at an earlier stage can prevent many associated health problems. There are significant changes in the multiple bio-signals, such as electrical, thermal, optical, etc., when an individual is under stress. Such bio-signals can be utilized to identify stress. In this paper, we propose a multi-layered deep learning-based approach for detecting human stress using the multimodal dataset. We use an open-source dataset, namely Wearable Stress and Affect Detection (WESAD), which contains data from wearable physiological and motion sensors. The modalities of these sensors include axis acceleration, body temperature, electrocardiogram, and electrodermal activity with three conditions: baseline, amusement, and stress. In the first layer of our multi-layered approach, we train and compare AutoEncoder and Variational AutoEncoder to learn the normal emotional state of the subject. In the second layer, we train and compare LSTM and Transformer models to classify the subjects as either in an amused or stressed state. This multi-layered approach helps to achieve a higher stress detection rate of 98%.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Liapis, A., Katsanos, C., Karousos, N., Xenos, M., Orphanoudakis, T.: User experience evaluation: a validation study of a tool-based approach for automatic stress detection using physiological signals. Int. J. Human-Comput. Interact. 1–14 (2020)
Gjoreski, M., et al.: Datasets for cognitive load inference using wearable sensors and psychological traits. Appl. Sci. 10, 3843 (2020)
Cho, Y., Bianchi-Berthouze, N., Julier, S.J.: DeepBreath: deep learning of breathing patterns for automatic stress recognition using low-cost thermal imaging in unconstrained settings. In: 2017 7th International Conference on Affective Computing and Intelligent Interaction (ACII), pp. 456- 463. IEEE (2017)
Lin, H., et al.: Detecting stress based on social interactions in social networks. IEEE Trans. Knowl. Data Eng. 29(9), 1820–1833 (2017)
Siirtola, P.: Continuous stress detection using the sensors of commercial smartwatch. In: Adjunct Proceedings of the 2019 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2019 ACM International Symposium on Wearable Computers, pp. 1198–1201 (2019)
Singh, M., Queyam, A.B.: A novel method of stress detection using physiological measurements of automobile drivers. Int. J. Electron. Eng. 5(2), 13–20 (2013)
Padmaja, B., Prasad, V.R., Sunitha, K.V.: A machine learning approach for stress detection using a wireless physical activity tracker. Int. J. Mach. Learn. Comput. 8, 33–38 (2018)
Giannakakisa, G., Pediaditisa, M., Manousos, D.: Stress and anxiety detection using facial cues from videos. Elsevier (2016)
Wijsman, J., Grundlehner, B., Liu, H.: Towards mental stress detection using wearable physiological sensors. IEEE (2011)
Barreto, A., Zhai, J., Adjouadi, M.: Non-intrusive physiological monitoring for automated stress detection in human-computer interaction. In: Lew, M., Sebe, N., Huang, T.S., Bakker, E.M. (eds.) HCI 2007. LNCS, vol. 4796, pp. 29–38. Springer, Heidelberg (2007). https://doi.org/10.1007/978-3-540-75773-3_4
Ciabattoni, L., Ferracuti, F., Longhi, S., Pepa, L., Romeo, L., Verdini, F.: Real-time mental stress detection based on smartwatch. In: 2017 IEEE International Conference on Consumer Electronics (ICCE), pp. 110–111. IEEE (2017)
Werner, P., Al-Hamadi, A., Niese, R., Walter, S., Gruss, S., Traue, H.C.: Automatic pain recognition from video and biomedical signals. In: 2014 22nd International Conference on Pattern Recognition, pp. 4582–4587 (2014)
Rastgoo, M.N., Nakisa, B., Maire, F., Rakotonirainy, A., Chandran, V.: Automatic driver stress level classification using multimodal deep learning. Expert Syst. Appl. 138, 112793 (2019)
Umematsu, T., Sano, A., Taylor, S., Picard, R.W.: Improving students’ daily life stress forecasting using LSTM neural networks. In: 2019 IEEE EMBS IC on Biomedical & Health Informatics (BHI), pp. 1–4. IEEE (2019)
Schmidt, P., Reiss, A., Duerichen, R., et al.: Introducing wesad, a multimodal dataset for wearable stress and affect detection. In: Proceedings of the 20th ACM International Conference on Multimodal Interaction, 400–408 (2018)
Zaremba, W., Sutskever, I., Vinyals, O.: Recurrent neural network regularization. arXiv preprint arXiv:1409.2329. 8 Sep 2014
Greff, K., Srivastava, R.K., KoutnÃk, J., Steunebrink, B.R., Schmidhuber, J.: LSTM: a search space odyssey. IEEE Trans. Neural Netw. Learn. Syst. 28(10), 2222–2232 (2016)
Soni, J., Prabakar, N., Upadhyay, H.: Behavioral analysis of system call sequences using lstm seq-seq, cosine similarity and jaccard similarity for real-time anomaly detection In: International Conference on Computational Science and Computational Intelligence (CSCI), pp. 214–219 (2019).https://doi.org/10.1109/CSCI49370.2019.00043
Soni, J., Prabakar, N., Upadhyay, H.: Visualizing high-dimensional data using t-distributed stochastic neighbor embedding algorithm. In: Arabnia, H.R., Daimi, K., Stahlbock, R., Soviany, C., Heilig, L., Brüssau, K. (eds.) Principles of Data Science. TCSCI, pp. 189–206. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-43981-1_9
Liapis, A., Faliagka, E., Katsanos, C., Antonopoulos, C., Voros, N.: Detection of subtle stress episodes during ux evaluation: assessing the performance of the wesad bio-signals dataset. In: Ardito, C., et al. (eds.) INTERACT 2021. LNCS, vol. 12934, pp. 238–247. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-85613-7_17
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Soni, J., Prabakar, N., Upadhyay, H. (2023). A Multi-layered Deep Learning Approach for Human Stress Detection. In: Zaynidinov, H., Singh, M., Tiwary, U.S., Singh, D. (eds) Intelligent Human Computer Interaction. IHCI 2022. Lecture Notes in Computer Science, vol 13741. Springer, Cham. https://doi.org/10.1007/978-3-031-27199-1_2
Download citation
DOI: https://doi.org/10.1007/978-3-031-27199-1_2
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-27198-4
Online ISBN: 978-3-031-27199-1
eBook Packages: Computer ScienceComputer Science (R0)