Virtual Skiing as an Art Installation

The Virtual Skiing game allows the user to immerse himself into the skiing sensation without using any obvious hardware interfaces. To achieve the movement down the virtual skiing slope the skier who stands on a pair of skis attached to the floor performs the same movements as on real skis, in particular this is the case on carving skis: tilting the body to the left initiates a left turn, tilting the body to the right initiates a right turn, by lowering the body, the speed is increased. The skier observes his progress down the virtual slope projected on the wall in front of him. The skier’s movements are recorded using a video camera placed in front of him and processed on a PC in real time to drive the projected animation of the virtual slope

SELF-IMAGE MULTIMEDIA TECHNOLOGIES FOR FEEDFORWARD OBSERVATIONAL LEARNING

This dissertation investigates the development and use of self-images in augmented reality systems for learning and learning-based activities. This work focuses on self- modeling, a particular form of learning, actively employed in various settings for therapy or teaching. In particular, this work aims to develop novel multimedia systems to support the display and rendering of augmented self-images. It aims to use interactivity (via games) as a means of obtaining imagery for use in creating augmented self-images. Two multimedia systems are developed, discussed and analyzed. The proposed systems are validated in terms of their technical innovation and their clinical efficacy in delivering behavioral interventions for young children on the autism spectrum

Adaptable videogame platform for interactive upper extremity rehabilitation

The primary objective of this work is to design a recreational rehabilitation videogame platform for customizing motivating games that interactively encourage purposeful upper extremity gross motor movements. Virtual reality (VR) technology is a popular application for rehabilitation therapies but there is a constant need for more accessible and affordable systems. We have developed a recreational VR game platform can be used as an independent therapy supplement without laboratory equipment and is inexpensive, motivating, and adaptable. The behaviors and interactive features can be easily modified and customized based on players\u27 limitations or progress. A real-time method of capturing hand movements using programmed color detection mechanisms to create the simulated virtual environments (VEs) is implemented. Color markers are tracked and simultaneously given coordinates in the VE where the player sees representations of their hands and other interacting objects whose behaviors can be customized and adapted to fit therapeutic objectives and players\u27 interests. After gross motor task repetition and involvement in the adaptable games, mobility of the upper extremities may improve. The videogame platform is expanded and optimized to allow modifications to base inputs and algorithms for object interactions through graphical user interfaces, thus providing the adaptable need in VR rehabilitation

A Person-Centric Design Framework for At-Home Motor Learning in Serious Games

abstract: In motor learning, real-time multi-modal feedback is a critical element in guided training. Serious games have been introduced as a platform for at-home motor training due to their highly interactive and multi-modal nature. This dissertation explores the design of a multimodal environment for at-home training in which an autonomous system observes and guides the user in the place of a live trainer, providing real-time assessment, feedback and difficulty adaptation as the subject masters a motor skill. After an in-depth review of the latest solutions in this field, this dissertation proposes a person-centric approach to the design of this environment, in contrast to the standard techniques implemented in related work, to address many of the limitations of these approaches. The unique advantages and restrictions of this approach are presented in the form of a case study in which a system entitled the "Autonomous Training Assistant" consisting of both hardware and software for guided at-home motor learning is designed and adapted for a specific individual and trainer. In this work, the design of an autonomous motor learning environment is approached from three areas: motor assessment, multimodal feedback, and serious game design. For motor assessment, a 3-dimensional assessment framework is proposed which comprises of 2 spatial (posture, progression) and 1 temporal (pacing) domains of real-time motor assessment. For multimodal feedback, a rod-shaped device called the "Intelligent Stick" is combined with an audio-visual interface to provide feedback to the subject in three domains (audio, visual, haptic). Feedback domains are mapped to modalities and feedback is provided whenever the user's performance deviates from the ideal performance level by an adaptive threshold. Approaches for multi-modal integration and feedback fading are discussed. Finally, a novel approach for stealth adaptation in serious game design is presented. This approach allows serious games to incorporate motor tasks in a more natural way, facilitating self-assessment by the subject. An evaluation of three different stealth adaptation approaches are presented and evaluated using the flow-state ratio metric. The dissertation concludes with directions for future work in the integration of stealth adaptation techniques across the field of exergames.Dissertation/ThesisDoctoral Dissertation Computer Science 201