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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- 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.
- Pre-diagnosis of diabetic retinopathy implementing supervised learning algorithms using an ocular fundus Latin-American dataset for cross-data validation(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2021-02) De la Cruz Espinosa, Emanuel; FUENTES AGUILAR, RITA QUETZIQUEL; 229297; Fuentes Aguilar, Rita Quetziquel; emipsanchez; García González, Alejandro; Ochoa Ruiz, Gilberto; Abaunza González, Hernán; School of Engineering and Sciences; Campus MonterreyNowadays diabetes is a disease with worldwide presence and high mortality rate, causing a big social and economic impact. One of the major negative effects of diabetes is visual loss due to diabetic retinopathy (DR). To prevent this condition is necessary to identify referable patients by screening for DR, and complementing with an Optic Coherence Tomography (OCT), that is another study to perform an early detection of blindness doing several longitudinal scans at a series of lateral locations to generate a map of reflection sites in the sample and display it as a two-dimensional image achieving transmission images in turbid tissue. Regrettably the number of ophthalmologists and OCT devices is not enough to provide an adequate health care to the diabetic population. Although there exist AI systems capable of do DR screening, they do not aim the assessment specifically in macula area considering visible and proliferated anomalies, signs of high damage and late intervention. This work presents three surpevised machine learnig algorithms; a Random Forest (RF) classifier, a Convolutional Neural Network (CNN) model, and a transfer learning (TL) pretrained model able to sort fundus images in three classes as an fundus images exclusive database is labeled. Processing techniques such as channel splitting, color space transforms, histogram and spatial based filters and data augmentation are used in order to detect presence of diabetic retinopathy. The stages of this work are: Publicly available dataset debugging, macular segmentation and cropping, data pre-processing, features extraction, model training, test and validation performance evaluation with a exclusive Latin-American dataset considering accuracy, sensitivity and specificity as metrics. The best results achieved are a 61.22% of accuracy, 86.67% of sensitivity and 89.47% of specificity.