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.
- Monitoring and diagnosis of the well-being with biosensors and intelligent systems(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2022-01-08) Machado Jaimes, Lizeth Guadalupe; Alfaro Ponce, Mariel; puemcuervo, emipsanchez; Argüelles Cruz, Amadeo José; School of Engineering and Sciences; Campus Ciudad de México; Bustamante Bello, RogelioNowadays, society is more aware about their wellbeing and health, making wearable devices an unexploited and affordable way to continuously monitor them. Smartwatches have gained popularity among wearable devices, enabling access to daily vital physiological measurements, which help people be aware of their health condition. Offering non-invasive, real-time daily monitoring,providing health-related data that may be used to identify a lack of stability in the body, whether it is physical or mental. This project introduces LM Research, a smart monitoring system consisted mainly of a webpage, REST APIs, machine learning algorithms and smartwatches. This system monitors users’ physical and mental indicators to prevent a potential well-being crisis. This will be accomplished by collecting psychological parameters in smartwatches and mental health data using a psychological questionnaire to further develop a supervised machine learning well-being model that will forecast smartwatch users’ well-being. The use of sensors in smartwatches provides an accurate measure of physiological functions of the body; for this reason, a well-established Brand (Garmin) was selected due to its high-quality sensors, which provide more accurate data in contrast with more economical alternatives. This research focuses on determining the most important physical and personal parameters that impact a person’s well-being by feature selection, which will be fed to the machine learning forecasting model. To engage with users and acquire all the data needed to predict their well-being, a website was built and housed in the cloud, allowing the creation of a larger and reachable dataset. In contrast to building the project in a local computing environment, which has more constraints such as data storage and processing, cloud computing makes it scalable, flexible and mobile due to using external servers’ capability.