Ciencias Exactas y Ciencias de la Salud
Permanent URI for this collectionhttps://hdl.handle.net/11285/551039
Pertenecen a esta colección Tesis y Trabajos de grado de las Maestrías correspondientes a las Escuelas de Ingeniería y Ciencias así como a Medicina y Ciencias de la Salud.
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- Facilitating early detection of depression through conversational audios and machine learning techniques(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2023-06-21) Noriega Quirós, Isabella; Trejo Rodriguez, Luis Ángel; puemcuervo, emipsanchez; González Mendoza, Miguel; Brena Pinero, Ramón Felipe; Figueroa López, Carlos Gonzalo; Escuela de Ingeniería y Ciencias; Campus MonterreyMental health is becoming a trending topic amongst society. The relevance of it in our lives is being studied in order to achieve a better comprehension for our well-being. Studies have shown that both anxiety and depression greatly affect higher education student’s performance and development, as well as post-graduate life. Early detection of depression, or other mental health issues, could lead to sooner evaluation and support. As humans go through life, many stressful situations arise. This is not possible to avoid. Nevertheless, our resilience to stress is the factor that estimates how much stress we can handle until reaching alerting levels of a possible mental disorder. This research intends to use machine learning techniques to deliver an accurate classification from depressive indicators based on conversational audios. The result provided will be used by an algorithm to analyze the individual’s state, and with the combination of conversational audios and the psychophysiological profile, it will identify early symptoms of the illness, which will alert the individual in time to act.
- 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, GustavoVirtual 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.
- Stress levels detection from EMG signals in virtual reality: a machine learning approach(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2022) Orozco Mora, Carmen Elisa; Fuentes Aguilar, Rita Quetziquel; emijzarate, emipsanchez; Tovar Corona, Blanca; González Mendoza, Miguel; Escuela de Ingeniería y Ciencias; Campus Estado de México; Hernández Melgarejo, GustavoVirtual Reality technology has had an outbreak in mainstream usage in the last decade, nowadays it is being used in areas like entertainment, education, rehabilitation, etc. In the entertainment area, Virtual Reality is implemented in video games to enhance the enjoyment of the player and has become an important asset in the industry since there exists a constant search for ways to improve gameplay and immersion experience. The difficulty level of a video game has a direct impact on the enjoyment and feeling of flow of the players, if they find the game too hard or too easy they can lose interest and leave with a bad impression of the game, but if the difficulty was to adapt to their levels of stress then the flow, enjoyment and thus, feeling of presence of the player would be enhanced. The analysis of some physiological signals that have a direct correlation with the emotional state of a person and could help with the assessment of their stress level. There exist previous studies where they managed the latter using different Machine Learning techniques and obtaining different results and levels of accuracy. Nonetheless, there does not exist a forearm-based electromyogram signal classifier that determines the level of stress of a Virtual Reality video game player. In this study, a Virtual Reality video game was built, it was designed to induce three different levels of stress in the player. This game was used to compile a dataset containing features extracted from the forearm EMG signals from volunteers while playing said video game. Different Machine Learning algorithms were trained and tested for classification. We obtained up to 66.6% accuracy in the 4-class classification between three levels of stress and a resting stage and 100% accuracy in the binary classification of a resting stage and the different levels of stress. The best-performing models were embedded in an external system to make predictions online. These were validated through experimental and statistical tests. Finally, a system was built which allowed to dynamically change the difficulty of a VR video game accordingly to the predicted level of stress of the player. We concluded that the system was capable of reacting as expected and that the difficulty of a game can indeed be modified online with the input of the emotional state of the player.
- 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 MonterreyStress 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.