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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- Analyzing VR and AR I4.0 technologies for industrial applications: A comparative study and selection approach development(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2024-12-11) Chavez Najera, Daniela Monserrat; Ahuett Garza, Horacio; emipsanchez; Urbina Coronado, Pedro Daniel; Orta Castañón, Pedro Antonio; School of Engineering and Sciences; Campus MonterreyIn recent years, the implementation of immersive technologies such as Virtual Reality (VR) and Augmented Reality (AR) for Industry 4.0 (I4.0) applications has increased considerably. These technologies enable the connection of virtual and real environments focusing on human centered manufacturing. A challenge when implementing immersive technologies in industrial tasks is the lack of clear paths to select the most appropriate technology for specific operations, and the nonexistence of metrics to evaluate the integration performance. Nonetheless, there are trends in the literature that offer insights to conduct the decision making process for selection between immersive technologies, ensuring the suitability of the application. Based on the decision criteria identified in the literature a decision making approach is developed. This thesis also presents the development workflow of three VR/AR applications implemented in Unity Engine for Meta Quest 3 and Hololens 2. These applications are evaluated using overall performance metrics and are analyzed using the proposed approach.
- Control of a virtual reality environment for upper limb movement using a motor imagery-based brain computer interface(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2023-06-01) Mancha Mendoza, Oscar Andres; Antelis, Javier M.; puemcuervo, emimayorquin; García González, Alejandro; Fuentes Aguilar, Rita Q.; Morales, Manuel D.; School of Engineering and Sciences; Campus Monterrey; Mendoza Montoya, OmarThe following project is presented as a thesis proposal for the Master of Science in Computer Science (MCC-i): the design, implementation and evaluation of a virtual reality (VR) environment controlled using a Motor Imagery based Brain Computer Interface (BCI). BCIs enhance the effective communication and interaction between humans and computers. Such systems are increasingly prevalent in diverse applications, including education, entertainment, and health. The aim of this system is to reate a rehabilitation environment for upper limb motor recovery, in the form of a VR game. In the system, the user performs left and right arm MI, which is detected using machine learning algorithms to perform a movement and ability within the virtual environment. The system was evaluated with five healthy participants in one experimental session each. Each experimental session consisted of a training and an evaluation routine, in which the participants were asked to imagine each MI task andomly to gather training data and then, with the trained classification algorithm, the users were evaluated by playing the game in which they needed to perform the correct ability in the game to attack an enemy. The classification algorithms showed a ranged accuracy of 39.6% to 68.6%, with an average of 51.9%; the evaluation accuracy ranged from 38.5% to 76.9%, with an average of 58.5%. A User Experience Survey was applied to the participants, obtaining positive results and feedback on game improvements.
- 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.
- Virtual Reality environment for the analysis of preoperative studies through a 3D volumetric visualization(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2021-06-14) Cruz Díaz, Sirenia Guadalupe; Cortés Ramírez, Jorge Armando; tolmquevedo; Jiménez Vielma, Julio Fernando; Presbítero Espinosa, Gerardo; School of Engineering and Sciences; Campus MonterreyIt is rare to interpret Computed Tomography (CT) images in a different way than the traditional approach since the technology exists to do it faster and better, the requisite equipment and software are expensive, and not all hospitals can afford them. The following work presents a virtual reality environment for the analysis of preoperative studies through a 3D volumetric visualization, using open-source software to contribute to the optimal visualization of Computed Tomography (CT) scans, in order to promote a quick and efficient interpretation.