Abstract
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 () 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.
Highlights
Stress has become a primary topic in the pursuit of mental health for modern society.The main reason for this is that stressful events can act as a precursor to major psychiatric conditions, such as anxiety and depression [1]
Even though measuring stress has been previously studied [3,15] with advanced statistics and machine learning tools, few studies, such as the one from Ćosić et al [7], consider the evaluation of resilience to stress based on physiological response in individuals
They are based on the idea that depending on the degree that individuals improve their resilience to stress, they will be more capable of coping with difficult situations and, as a result, be able to reduce their risk for depression or other mental disorders
Summary
The main reason for this is that stressful events can act as a precursor to major psychiatric conditions, such as anxiety and depression [1]. These mental illnesses can lead to physical problems, such as an increase in the risk for cardiovascular diseases [1,2]. Multiple works have examined stress detection [1,2,3,4,5,6], we have identified that the variables explored in these studies have not measured resilience to the stress of an individual [3,4,5,6]. Ćosić et al [7] identified that there is no direct quantifiable index to monitor the degree of resilience to stress of an individual based on physiological responses
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