Jing H, et al. Physiological and molecular responses to hypoxia stress in Manila clam ruditapes philippinarum. Aquat Toxicol. 2023: 106428.

Pisanski K, et al. Multimodal stress detection: testing for covariation in vocal, hormonal and physiological responses to Trier Social stress test. Horm Behav. 2018;106:52–61.

Article  Google Scholar 

Naegelin M et al. An interpretable machine learning approach to multimodal stress detection in a simulated office environment. J Biomed Inf, p. 2023:104299.

Zhu J, et al. Physiological and anatomical changes in two rapeseed (Brassica napus L.) genotypes under drought stress conditions. Oil Crop Sci. 2021;6(2):97–104.

Article  Google Scholar 

Zontone P, Affanni A, Bernardini R, Piras A, Rinaldo R. Stress detection through electrodermal activity (EDA) and electrocardiogram (ECG) analysis in car drivers, in 2019 27th European Signal Processing Conference (EUSIPCO), pp. . 2019:1-5.

Anusha AS, et al. Electrodermal activity based pre-surgery stress detection using a wrist wearable. IEEE J Biomed Health Inf. 2019;24(1):92–100.

Google Scholar 

Faust O et al. Heart rate variability for medical decision support systems: a review. Comput Biol Med, p. 105407, 2022.

Wang C, et al. Impact of ozone exposure on heart rate variability and stress hormones: a randomized-crossover study. J Hazard Mater. 2022;421:126750.

Article  Google Scholar 

Bobade P, Vani M. Stress detection with machine learning and deep learning using multimodal physiological data, in 2020 Second International Conference on Inventive Research in Computing Applications (ICIRCA), 2020: pp. 51–57.

Jebelli H, Khalili MM, Lee S. A continuously updated, computationally efficient stress recognition framework using electroencephalogram (EEG) by applying online multitask learning algorithms (OMTL). IEEE J Biomed Health Inf. 2018;23(5):1928–39.

Article  Google Scholar 

Gedam S, Paul S. A review on mental stress detection using wearable sensors and machine learning techniques. IEEE Access. 2021;9:84045–66.

Article  Google Scholar 

Bakkialakshmi VS, Thalavaipillai S. AMIGOS: a robust emotion detection framework through Gaussian ResiNet. Bull Electr Eng Inf. 2022;11(4):2142–50.

Google Scholar 

Schmidt P, Reiss A, Duerichen R, Marberger C, Van Laerhoven K. Introducing wesad, a multimodal dataset for wearable stress and affect detection, in Proceedings of the 20th ACM international conference on multimodal interaction, 2018, pp. 400–408.

Wijsman J, Grundlehner B, Liu H, Hermens H, Penders J. Towards mental stress detection using wearable physiological sensors, in 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2011, pp. 1798–1801.

Koldijk S, Sappelli M, Verberne S, Neerincx MA, Kraaij W. The swell knowledge work dataset for stress and user modeling research, in Proceedings of the 16th international conference on multimodal interaction, 2014: pp. 291–298.

Sandulescu V, Andrews S, Ellis D, Bellotto N, Mozos OM, Part. Stress detection using wearable physiological sensors, in Artificial Computation in Biology and Medicine: International Work-Conference on the Interplay Between Natural and Artificial Computation, IWINAC 2015, Elche, Spain, June 1–5, 2015, Proceedings, Part I 6, 2015: pp. 526–532.

Garg P, Santhosh J, Dengel A, Ishimaru S. Stress detection by machine learning and wearable sensors, in Companion Proceedings of the 26th International Conference on Intelligent User Interfaces, 2021, pp. 43–45.

Fan T, et al. A new deep convolutional neural network incorporating attentional mechanisms for ECG emotion recognition. Comput Biol Med. 2023;159. https://doi.org/10.1016/j.compbiomed.2023.106938.

Kumar A, Sharma K, Sharma A. Hierarchical deep neural network for mental stress state detection using IoT based biomarkers. Pattern Recognit Lett. 2021;145. https://doi.org/10.1016/j.patrec.2021.01.030.

Lin J, Pan S, Lee CS, Oviatt S. An explainable deep fusion network for affect recognition using physiological signals, in Proceedings of the 28th ACM International Conference on Information and Knowledge Management, 2019, pp. 2069–2072.

Li R, Liu Z. Stress detection using deep neural networks. BMC Med Inf Decis Mak. 2020;20. https://doi.org/10.1186/s12911-020-01299-4.

Zou C, Deng Z, He B, Yan M, Wu J, Zhu Z. Emotion classification with multi-modal physiological signals using multi‐attention‐based neural network, Cognitive Computation and Systems, Jun. 2024, https://doi.org/10.1049/ccs2.12107

Sarkar P, Etemad A. Self-supervised ECG representation learning for emotion recognition. IEEE Trans Affect Comput. 2022;13(3). https://doi.org/10.1109/TAFFC.2020.3014842.

Zuur AF, Ieno EN, Elphick CS. A protocol for data exploration to avoid common statistical problems. Methods Ecol Evol. 2010;1(1):3–14.

Article  Google Scholar 

Panigrahi R, Borah S. Rank of normalizers through TOPSIS with the help of supervised classifiers, International Journal of Engineering and Technology(UAE), vol. 7, no. 3.24 Special Issue 24, pp. 483–490, 2018, https://doi.org/10.14419/ijet.v7i1.1.10150

Raschka S. MLxtend: providing machine learning and data science utilities and extensions to Python’s scientific computing stack. J Open Source Softw. Apr. 2018;3(24). https://doi.org/10.21105/joss.00638.