Key result
CNN-LSTM model predicts acute stress responses from personality traits with ~0.80 AUC.
Why the study?
Acute stress responses in emergency situations can waste precious seconds provided by earthquake early warning systems, motivating investigation into the correlation between personality and acute stress responses.
Observational
Personality traits significantly correlate with acute stress responses during simulated earthquakes, and deep learning models can predict these responses using HRV and respiration data.
Answers are grounded in this paper and cite their sources.
Supports personality-informed DL stress models in simulations; leaves open real-world validation and clinical translation.
Earthquakes, as significant natural disasters, still cannot be accurately predicted today. Although current earthquake early warning systems can provide alerts several seconds in advance, acute stress responses (ASR) in emergency situations can waste these precious escape seconds. To investigate the correlation between personality and ASR, this study collected the temperament and character of all participants using the Chen Huichang-60 Temperament Scale and the DISC Personality Inventory. In addition, this study simulated growing earthquakes in an earthquake experience hall, collecting heart rate variability and respiration signal variations throughout the process from subjects. Multivariate analysis of variance (MANOVA) and Toeplitz Inverse Covariance-Based Clustering methods were used to analyse the differences and connections between them. Furthermore, this study employed a deep learning model that combines Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) to predict ASR across personalities. This model used datasets from the majority dataset of a certain personality and a single participant, respectively, and showed different performance. The results are as follows. After categorising participants based on personality test results, MANOVA revealed significant differences between the personality groups Influence-Choleric and Influence-Sanguine (p = 0.001), Influence-Phlegmatic and Steadiness-Sanguine (p = 0.023), Influence-Sanguine and Steadiness-Sanguine (p < 0.001) and Influence-Sanguine and Steadiness-Phlegmatic (p < 0.001), as well as across different earthquake stages (p < 0.01). The clustering method quantified stress responses over time for different personalities and labelled ASR levels for use in supervised learning. Ultimately, the CNN-LSTM model performed predictions of ASR using both personality and individual datasets, achieving the AUC of 0.795 and 0.72, demonstrating better prediction and classification effectiveness with the former. This study provides a new personality-based method for earthquake stress management, creating possibilities for longitudinal stress research and prediction. It aids the general public in comprehending their own acute stress and allows authorities and communities to make practical, efficient disaster evacuation plans based on the overall situation of public ASR.
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Li et al. (2024) conducted an observational in Acute stress responses. Personality-based CNN-LSTM prediction model vs. Individual participant dataset model was evaluated on Prediction of acute stress responses (ASR) (AUC 0.795). A CNN-LSTM deep learning model predicted acute stress responses during simulated earthquakes based on personality traits, achieving an AUC of 0.795 compared to 0.72 for individual datasets.
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