One step closer to mental health: resilience to mental stress index in the face of analytical problems, a machine learning approach

Abstract

Stress and depression are two major topics of concern for higher education institutions. Studies have shown how mental health problems can decrease students' ability to function efficiently during their education life and how depression can risk their physical well-being. To aid students in coping with the challenging experience of higher education and therefore enable them to perform better in stressful situations post-graduation, researchers recommend increasing their level of resilience.

In an attempt to measure a person's resilience, previous studies have developed and analyzed self-rating questionnaires. While these studies have provided a way to assess people's psychological responses and provided a significant amount of insight, they do not provide an objective measurement for resilience to mental stress. There have been related studies that have evaluated physiological signals in individuals and have identified relationships with people's stress. Based on previous literature and applying machine learning, this thesis aims to demonstrate the feasibility of measuring an individual's resilience to mental stress and proposes a Resilience to Mental Stress Index (RMSI). In addition to this, this thesis presents an experiment's methodology to collect physiological and psychological data using smartwatch embedded sensors and psychological tools to study depression prediction.

This research performed data analysis of 71 individuals subjected to a 10-minute psychophysiological test to study resilience to mental stress. The data collected considers five physiological features: (a) muscle response (electromyography), (b) blood volume pulse, (c) breathing rate, (d) peripheral temperature, and (e)skin conductance. We utilized unsupervised learning techniques to visualize and identify the relationship between these feature variability. As a result of the analysis, we created three different methods for the RMSI. The results' analysis between the different methods showed no statistically significant difference (p>0.05). However, we recommend using the Mahalanobis distance (MD) method because of its relationship with the validation methods. Even though there exists no standard method to quantify resilience to mental stress, our results indicate a positive relationship to the Resilience in Mexicans (RESI-M) psychological tool.

For the study of depression prediction, during this research, five variables were selected for the study: (a) personality traits, (b) RMSI, (c) heart rate variability (HRV), and (d) sleeping disorders. To collect these variables, we developed a methodological framework and built a mobile application. We hope that this research serves as a solid baseline to understand resilience to mental stress and collect valuable information to predict depression.