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

SynApps: Children’s ASD Management Through A Mobile Application and An Interactive Biofeedback Exercise

This thesis explores how to provide better access to alternative treatment options through an immersive biofeedback exercise and mobile application for children with autism spectrum disorders (ASD). This thesis also seeks to connect children with ASD to medical professionals and treatment facilities. This is done through a motion graphics piece depicting an abdominal breathing exercise and correlating mobile application. Users participate in biofeedback exercises in which their real-time biofeedback is monitored, transmitted to the mobile application, and reflected to the user. The mobile application analyzes and identifies physiological signals and detects what feedback is most useful to help the individual meet goals of increased vagal tones. Increased vagal tones correlates with high frequency heart rate variability. The game promotes self-awareness and self-regulation through abdominal breathing, heart rate variability, and mindfulness. If done consistently, biofeedback exercises may help re-train the body’s response to stressful situations, leading to function and behavioral improvements in everyday life. Additionally, this treatment application aims to be a preventative health option for children with ASD as a means to hopefully diminish the lifetime costs of care. This topic is important to the field of design and the broader community because it utilizes the power of design, motion, and interactive technology to potentially bring about improvements to the lives of children with ASD. Furthermore, this thesis unifies design with science as a means to bring better health care access and support services to people with ASD

Wearable Computing for Health and Fitness: Exploring the Relationship between Data and Human Behaviour

Health and fitness wearable technology has recently advanced, making it easier for an individual to monitor their behaviours. Previously self generated data interacts with the user to motivate positive behaviour change, but issues arise when relating this to long term mention of wearable devices. Previous studies within this area are discussed. We also consider a new approach where data is used to support instead of motivate, through monitoring and logging to encourage reflection. Based on issues highlighted, we then make recommendations on the direction in which future work could be most beneficial

Machine learning methods for the study of cybersickness: a systematic review

This systematic review offers a world-first critical analysis of machine learning methods and systems, along with future directions for the study of cybersickness induced by virtual reality (VR). VR is becoming increasingly popular and is an important part of current advances in human training, therapies, entertainment, and access to the metaverse. Usage of this technology is limited by cybersickness, a common debilitating condition experienced upon VR immersion. Cybersickness is accompanied by a mix of symptoms including nausea, dizziness, fatigue and oculomotor disturbances. Machine learning can be used to identify cybersickness and is a step towards overcoming these physiological limitations. Practical implementation of this is possible with optimised data collection from wearable devices and appropriate algorithms that incorporate advanced machine learning approaches. The present systematic review focuses on 26 selected studies. These concern machine learning of biometric and neuro-physiological signals obtained from wearable devices for the automatic identification of cybersickness. The methods, data processing and machine learning architecture, as well as suggestions for future exploration on detection and prediction of cybersickness are explored. A wide range of immersion environments, participant activity, features and machine learning architectures were identified. Although models for cybersickness detection have been developed, literature still lacks a model for the prediction of first-instance events. Future research is pointed towards goal-oriented data selection and labelling, as well as the use of brain-inspired spiking neural network models to achieve better accuracy and understanding of complex spatio-temporal brain processes related to cybersickness