Ciencias Exactas y Ciencias de la Salud

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Emotion recognition based on physiological signals for Virtual Reality applications
(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2022-06-13) Oceguera Cuevas, Daniela; FUENTES AGUILAR, RITA QUETZIQUEL; 229297; Fuentes Aguilar, Rita Quetziquel; puemcuervo; Antelis Ortíz, Javier Mauricio; Fernández Cervantes, Victor; School of Engineering and Sciences; Campus Monterrey; Hernández Melgarejo, Gustavo
Virtual Reality (VR) Systems have been used in the last years with an increasing frequency because they can be implemented for multiple applications in various fields. Some of these include aerospace, military, psychology, education, and entertainment. A way to increase the sense of presence is to induce emotions through the VE, and since one of the main purposes of VR Systems is to evoke the same emotions as a real experience would, the induction of emotions and emotion recognition could be used to enhance the experience. The emotion of a user can be recognized through the analysis and processing of physiological signals such as Electrocardiogram (ECG) and Electrodermal Activity (EDA) signals. However, very few systems that present online feedback regarding the subject’s emotional state and the possibility of adapting the VE during user experience have been developed. This thesis proposes the development of a Virtual Reality video game that can be dynamically modified according to the physiological signals of a user to regulate his emotional state. The first experiment served for the creation of a database. Previous studies have shown that specific features from these signals, can be used to develop algorithms capable of classifying the emotional states of the subjects into multiple classes or the two emotional dimensions: valence and arousal. Thus, this experiment helped to develop an appropriate Virtual Reality video game for stress induction, a signal acquisition, and conditioning system, a signal processing model and to extract time-domain signal features offline. A statistical analysis was performed to find significant differences between game stages and machine learning algorithms were trained and tested to perform classification offline. A second experiment was performed for the Proof of Concept Validation. For this, a model was created to extract features online and the classification algorithms were re-fitted with the online extracted features. Additionally, to facilitate a completely online process, the signal processing and feature extraction models were embedded on an STM32F446 Nucleo board, a strategy was implemented to dynamically modify the VE of the Virtual Reality video game according to the detected class, and the complete system was tested.
One step closer to mental health: resilience to mental stress index in the face of analytical problems, a machine learning approach
(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2021-06-01) Díaz Ramos, Ramón Eduardo; Trejo Rodríguez, Luis Angel; puemcuervo; Medina Pérez, Miguel Angel; González Mendoza, Miguel; Figueroa López, Carlos Gonzalo; School of Engineering and Sciences; Campus Monterrey
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.

Ciencias Exactas y Ciencias de la Salud

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