EnviroScape: Coping With Stress Using Implicit Biofeedback Application

Stress has been identified by the Word Health Organization as an epidemic that has negative impacts on work productivity. It costs the American industry approximately $300 billion/year and is also the leading contributor to obesity and cardiovascular diseases. Current stress remediation tools incorporate techniques such as deep breathing, meditation and biofeedback responses. These type of exercises require a substantial amount of time and resources along with adhering to their strict system in order to see results. Most biofeedback mechanisms are repetitive and mundane and also require complex equipment to participate, in order to receive proper evaluation on stress levels. The purpose of this study is to develop an engaging relaxation technique and analyze the effects of the biofeedback mechanism on the stress levels of a user. An interactive application is developed such that the user receives subtle cues when they are in a “stressed” state, which is determined through the physiological indicator of the user’s breathing rate (BR) signal. Unlike previous research, this biofeedback game focuses on providing a soothing natural environment with no specific objectives in order to distract them from their current stressful state. This will help analyze and discuss the effects of a non-competitive video game on a user’s stress levels, their awareness to recognize signs of stress and their ability to reduce them

Physiological Self Regulation with Biofeedback Games

Mental stress is a global epidemic that can have serious health consequences including cardiovascular diseases and diabetes. Several techniques are available to teach stress self-regulation skills including therapy, meditation, deep breathing, and biofeedback. While effective, these methods suffer from high drop-outs due to the monotonic nature of the exercises and are generally practiced in quiet relaxed environment, which may not transfer to real-world scenarios. To address these issues, this dissertation presents a novel intervention for stress training using games and wearable sensors. The approach consists of monitoring the user’s physiological signals during gameplay, mapping them into estimates of stress levels, and adapting the game in a way that promotes states of low arousal. This approach offers two key advantages. First, it allows users to focus on the gameplay rather than on monitoring their physiological signals, which makes the training far more engaging. More importantly, it teaches users to self-regulate their stress response, while performing a task designed to increase arousal. Within this broad framework, this dissertation studies three specific problems. First, the dissertation evaluates three physiological signals (breathing rate, heart rate variability, and electrodermal activity) that span across the dimensions of degrees of selectivity in measuring arousal and voluntary control in their effectiveness in lowering arousal. This will identify the signal appropriate for game based stress training and the associated bio-signal processing techniques for real-time arousal estimation. Second, this dissertation investigates different methods of biofeedback presentation e.g. visual feedback and game adaptation during gameplay. Selection of appropriate biofeedback mechanism is critical since it provides the necessary information to improve the perception of visceral states (e.g. stress) to the user. Furthermore, these modalities facilitate skill acquisition in distinct ways (i.e., top-down and bottom-up learning) and influence retention of skills. Third, this dissertation studies reinforcement scheduling in a game and its effect on skill learning and retention. A reinforcement schedule determines which occurrences of the target response are reinforced. This study focuses on continuous and partial reinforcement schedules in GBF and their effect on resistance to extinction (i.e. ability to retain learned skills) after the biofeedback is removed. The main contribution of this dissertation is in demonstrating that stress self-regulation training can be embedded in videogames and help individuals develop more adaptive responses to reduce physiological stress encountered both at home and work

Physiological Self Regulation with Biofeedback Games

Mental stress is a global epidemic that can have serious health consequences including cardiovascular diseases and diabetes. Several techniques are available to teach stress self-regulation skills including therapy, meditation, deep breathing, and biofeedback. While effective, these methods suffer from high drop-outs due to the monotonic nature of the exercises and are generally practiced in quiet relaxed environment, which may not transfer to real-world scenarios. To address these issues, this dissertation presents a novel intervention for stress training using games and wearable sensors. The approach consists of monitoring the user’s physiological signals during gameplay, mapping them into estimates of stress levels, and adapting the game in a way that promotes states of low arousal. This approach offers two key advantages. First, it allows users to focus on the gameplay rather than on monitoring their physiological signals, which makes the training far more engaging. More importantly, it teaches users to self-regulate their stress response, while performing a task designed to increase arousal. Within this broad framework, this dissertation studies three specific problems. First, the dissertation evaluates three physiological signals (breathing rate, heart rate variability, and electrodermal activity) that span across the dimensions of degrees of selectivity in measuring arousal and voluntary control in their effectiveness in lowering arousal. This will identify the signal appropriate for game based stress training and the associated bio-signal processing techniques for real-time arousal estimation. Second, this dissertation investigates different methods of biofeedback presentation e.g. visual feedback and game adaptation during gameplay. Selection of appropriate biofeedback mechanism is critical since it provides the necessary information to improve the perception of visceral states (e.g. stress) to the user. Furthermore, these modalities facilitate skill acquisition in distinct ways (i.e., top-down and bottom-up learning) and influence retention of skills. Third, this dissertation studies reinforcement scheduling in a game and its effect on skill learning and retention. A reinforcement schedule determines which occurrences of the target response are reinforced. This study focuses on continuous and partial reinforcement schedules in GBF and their effect on resistance to extinction (i.e. ability to retain learned skills) after the biofeedback is removed. The main contribution of this dissertation is in demonstrating that stress self-regulation training can be embedded in videogames and help individuals develop more adaptive responses to reduce physiological stress encountered both at home and work

Modelado de trastornos neurodegenerativos a través de sistemas afectivos

El objetivo de este trabajo de fin de grado es estudiar como el aprendizaje profundo basado en dominios afectivos puede ayudar en diferentes tareas relacionadas con el análisis de expresiones faciales. Una de estas tareas será la detección de la enfermedad neurodegenerativa del Parkinson. Para conseguir nuestro objetivo empezamos el trabajo recopilando información sobre el estado del arte de los temas más importantes que íbamos a tratar: el análisis facial, dominios afectivos y Enfermedad de Parkinson. La literatura relacionada indica que los adultos mayores con Enfermedad de Parkinson tienen una menor expresividad facial, conocida como hipomimia. Para detectar la hipomimia y ser capaces de clasificar entre pacientes sanos y pacientes con la enfermedad, proponemos una serie de experimentos basados en los modelos de aprendizaje profundo para el análisis de expresiones faciales. Los experimentos se dividen en dos fases. En primer lugar se utilizarán dos bases de datos afectivas (Affectnet y CFEE) y redes neuronales pre-entrenadas (VGG y Resnet) para reconocimiento facial. Estos modelos se adaptarán al dominio afectivo a través de las bases de datos propuestas y las populares técnicas de Transfer Learning. Una vez obtenidos los resultados, se escogerá el modelo que mejor se adapte al escenario de Parkinson. Aprovechando las características aprendidas por el modelo, vamos se aplicará nuevamente la técnica de Transfer Learning en este caso para pasar del dominio afectivo al del Parkinson, quedándonos con todas las capas del modelo menos la última y añadiéndole un clasificador de dos salidas. Con este nuevo modelo vamos a realizar la segunda fase, la clasificación de una base de datos con pacientes sanos y pacientes con la Enfermedad de Parkinson. Gracias a este segundo experimento el modelo aprenderá características relacionadas con los pacientes con la Enfermedad de Parkinson. Finalmente se realizan las conclusiones acerca de lo que el modelo generado va a poder aportar y ayudar a la medicina y se proponen distintos temas para realizar un trabajo futuro acerca de esta investigación