Comparing Classic Machine Learning with Deep Learning for Stress Detection Using Wearable Sensors

Mohammadi, Maryam; Chubin, Morteza; Aghapanah Roudsari, Hamed

doi:10.22124/cse.2024.27392.1075

Document Type : Original Article

Authors

¹ Department of Information Technology Engineering, University of Guilan, Rasht, Iran

² Department of Telecommunication Engineering, University of Malayer, Hamedan, Iran

³ School of Advanced Technologies in Medicine, Isfahan University of Medical Sciences, Isfahan, Iran

https://doi.org/10.22124/cse.2024.27392.1075

Abstract

Based on conducted research, stress can have a significant impact on human relationships and human-related incidents. By identifying stress during daily activities such as driving, some incidents and accidents can be prevented. In this study, the PhysioNet database pertaining to drivers' heart rate during driving was utilized, and their features were extracted. Subsequently, the features underwent reduction using PCA and were compared using two artificial intelligence methods. The results, including accuracy, error, and validation credibility with fold-10 in four classes, were obtained for both neural network and deep learning approaches. In the feature extraction phase, 7 spatial features, 16 frequency features, and 64 wavelet features were employed. The classification result for the neural network achieved an accuracy of 90.8%±0.8. In the deep network, comprising one-dimensional CNN and Dense layers, with a fusion of raw signals and extracted features, the accuracy reached 96.3%±0.6. These findings indicate the superiority of deep learning over neural networks in this domain. This diagnostic system is suitable for portable and compact applications in individuals' daily activities.

Keywords

References

[1] Sapolsky, R. M. (1992). Stress, the aging brain, and the mechanisms of neuron death. the MIT Press.

[2] Dionísio, P. A., Amaral, J. D., & Rodrigues, C. M. P. (2021). Oxidative stress and regulated cell death in Parkinson’s disease. Ageing research reviews, 67, 101263.

[3] Eckerling, A., Ricon-Becker, I., Sorski, L., Sandbank, E., & Ben-Eliyahu, S. (2021). Stress and cancer: mechanisms, significance and future directions. Nature Reviews Cancer, 21(12), 767-785.

[4] O'Connor, D. B., Thayer, J. F., & Vedhara, K. (2021). Stress and health: A review of psychobiological processes. Annual review of psychology, 72(1), 663-688.

[5] Tan, S., Yu, X., Xu, Y., Xu, Y., & Jia, P. (2021). Micro-stress bonding analysis of high precision and lightweight mirrors. Optics express, 29(21), 33665-33678.

[6] Pagnamenta, S., Grønvik, K. B., Aminian, K., Vereijken, B., & Paraschiv-Ionescu, A. (2022). Putting temperature into the equation: development and validation of algorithms to distinguish non-wearing from inactivity and sleep in wearable sensors. Sensors, 22(3), 1117.

[7] Chauvenet, C., Etheve, G., Sedjai, M., & Sharma, M. (2017, April). G3-PLC based IoT sensor networks for SmartGrid. In 2017 IEEE International Symposium on Power Line Communications and its Applications (ISPLC) 1-6. IEEE.

[8] Nazemi, H., Joseph, A., Park, J., & Emadi, A. (2019). Advanced micro-and nano-gas sensor technology: A review. Sensors, 19(6), 1285.

[9] Kartsch, V., Tagliavini, G., Guermandi, M., Benatti, S., Rossi, D., & Benini, L. (2019). Biowolf: A sub-10-mw 8-channel advanced brain–computer interface platform with a nine-core processor and ble connectivity. IEEE transactions on biomedical circuits and systems, 13(5), 893-906.

[10] Mamdiwar, S. D., Shakruwala, Z., Chadha, U., Srinivasan, K., & Chang, C. Y. (2021). Recent advances on IoT-assisted wearable sensor systems for healthcare monitoring. Biosensors, 11(10), 372.

[11] Gargiulo, G. D., Gunawardana, U., O’Loughlin, A., Sadozai, M., Varaki, E. S., & Breen, P. P. (2015). A wearable contactless sensor suitable for continuous simultaneous monitoring of respiration and cardiac activity. Journal of Sensors, 2015(1), 151859.

[12] Sihem, N. I. T. A., Bitam, S., & Mellouk, A. (2019, October). A body area network for ubiquitous driver stress monitoring based on ECG signal. In 2019 International Conference on Wireless and Mobile Computing, Networking and Communications (WiMob) (pp. 1-6). IEEE.

[13] Sriramprakash, S., Prasanna, V. D., & Murthy, O. R. (2017). Stress detection in working people. Procedia computer science, 115, 359-366.

[14] Liu, X., Wang, H., Li, Z., & Qin, L. (2021). Deep learning in ECG diagnosis: A review. Knowledge-Based Systems, 227, 107187.

[15] Liu, Z., Cao, Q., Jin, Q., Lin, J., Lv, G., & Chen, K. (2023). Accurate detection of arrhythmias on raw electrocardiogram images: An aggregation attention multi-label model for diagnostic assistance. Medical Engineering & Physics, 114, 103964.

[16] Anbalagan, T., Nath, M. K., Vijayalakshmi, D., & Anbalagan, A. (2023). Analysis of various techniques for ECG signal in healthcare, past, present, and future. Biomedical Engineering Advances, 6, 100089.

[17] Subasi, A. (2019). Practical guide for biomedical signals analysis using machine learning techniques: A MATLAB based approach. Academic Press.

[18] Alday, E. A. P., Gu, A., Shah, A. J., Robichaux, C., Wong, A. K. I., Liu, C., ... & Reyna, M. A. (2020). Classification of 12-lead ecgs: the physionet/computing in cardiology challenge 2020. Physiological measurement, 41(12), 124003.

[19] Clifford, G. D., Liu, C., Moody, B., Springer, D., Silva, I., Li, Q., & Mark, R. G. (2016, September). Classification of normal/abnormal heart sound recordings: The PhysioNet/Computing in Cardiology Challenge 2016. In 2016 Computing in cardiology conference (CinC) 609-612. IEEE.

[20] Anowar, F., Sadaoui, S., & Selim, B. (2021). Conceptual and empirical comparison of dimensionality reduction algorithms (pca, kpca, lda, mds, svd, lle, isomap, le, ica, t-sne). Computer Science Review, 40, 100378.

[21] Golany, T., Radinsky, K., & Freedman, D. (2020, November). SimGANs: Simulator-based generative adversarial networks for ECG synthesis to improve deep ECG classification. In International Conference on Machine Learning (pp. 3597-3606). PMLR.

[22] Zhu, J., & Fan, W. (2021, July). ECG Data Modeling and Analyzing via Deep Representation Learning and Nonparametric Hidden Markov Models. In Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval, 1905-1909.

[23] Shen, Q., Qin, H., Wei, K., & Liu, G. (2021). Multiscale deep neural network for obstructive sleep apnea detection using RR interval from single-lead ECG signal. IEEE Transactions on Instrumentation and Measurement, 70, 1-13.

[24] Rahman, A. U., Asif, R. N., Sultan, K., Alsaif, S. A., Abbas, S., Khan, M. A., & Mosavi, A. (2022). ECG classification for detecting ECG arrhythmia empowered with deep learning approaches. Computational intelligence and neuroscience, 2022(1), 6852845.

[25] Weimann, K., & Conrad, T. O. (2021). Transfer learning for ECG classification. Scientific reports, 11(1), 5251.

[26] Ahmed, A. A., Ali, W., Abdullah, T. A., & Malebary, S. J. (2023). Classifying cardiac arrhythmia from ECG signal using 1D CNN deep learning model. Mathematics, 11(3), 562.

[27] Narotamo, H., Dias, M., Santos, R., Carreiro, A. V., Gamboa, H., & Silveira, M. (2024). Deep learning for ECG classification: A comparative study of 1D and 2D representations and multimodal fusion approaches. Biomedical Signal Processing and Control, 93, 106141.

Computational Sciences and Engineering

Comparing Classic Machine Learning with Deep Learning for Stress Detection Using Wearable Sensors

References

References

Volume 3, Issue 2
September 2023
Pages 189-199

Comparing Classic Machine Learning with Deep Learning for Stress Detection Using Wearable Sensors

References

References

Volume 3, Issue 2September 2023Pages 189-199

Volume 3, Issue 2
September 2023
Pages 189-199