Towards a Resilience to Stress Index Based on Physiological Response: A Machine Learning Approach

Sensors

Miguel Angel Medina-Pérez¹  Luis A. Trejo¹  Daniela A. Gomez-Cravioto²  Ramon E. Diaz-Ramos²  Carlos Figueroa López³ 

[1] Department of Computer Science, School of Engineering and Sciences, Campus Estado de México, Tecnologico de Monterrey, Atizapán 52926, Mexico;Department of Computer Science, School of Engineering and Sciences, Campus Monterrey, Tecnologico de Monterrey, Monterrey 64849, Mexico;Department of Psychology, School of Health, Campus Ciudad de México, Tecnologico de Monterrey, Ciudad de México 14380, Mexico;

关键词: resilience to stress;    physiological response;    machine learning;    clustering;   

DOI  :  10.3390/s21248293

来源: DOAJ

【 摘 要 】

This study proposes a new index to measure the resilience of an individual to stress, based on the changes of specific physiological variables. These variables include electromyography, which is the muscle response, blood volume pulse, breathing rate, peripheral temperature, and skin conductance. We measured the data with a biofeedback device from 71 individuals subjected to a 10-min psychophysiological stress test. The data exploration revealed that features’ variability among test phases could be observed in a two-dimensional space with Principal Components Analysis (PCA). In this work, we demonstrate that the values of each feature within a phase are well organized in clusters. The new index we propose, Resilience to Stress Index (RSI), is based on this observation. To compute the index, we used non-supervised machine learning methods to calculate the inter-cluster distances, specifically using the following four methods: Euclidean Distance of PCA, Mahalanobis Distance, Cluster Validity Index Distance, and Euclidean Distance of Kernel PCA. While there was no statistically significant difference ($p>0.01$) among the methods, we recommend using Mahalanobis, since this method provides higher monotonic association with the Resilience in Mexicans (RESI-M) scale. Results are encouraging since we demonstrated that the computation of a reliable RSI is possible. To validate the new index, we undertook two tasks: a comparison of the RSI against the RESI-M, and a Spearman correlation between phases one and five to determine if the behavior is resilient or not. The computation of the RSI of an individual has a broader scope in mind, and it is to understand and to support mental health. The benefits of having a metric that measures resilience to stress are multiple; for instance, to the extent that individuals can track their resilience to stress, they can improve their everyday life.

【 授权许可】

Unknown