| Summary: | abstract: As the application of interactive media systems expands to address broader problems in health, education and creative practice, they fall within a higher dimensional space for which it is inherently more complex to design. In response to this need an emerging area of interactive system design, referred to as experiential media systems, applies hybrid knowledge synthesized across multiple disciplines to address challenges relevant to daily experience. Interactive neurorehabilitation (INR) aims to enhance functional movement therapy by integrating detailed motion capture with interactive feedback in a manner that facilitates engagement and sensorimotor learning for those who have suffered neurologic injury. While INR shows great promise to advance the current state of therapies, a cohesive media design methodology for INR is missing due to the present lack of substantial evidence within the field. Using an experiential media based approach to draw knowledge from external disciplines, this dissertation proposes a compositional framework for authoring visual media for INR systems across contexts and applications within upper extremity stroke rehabilitation. The compositional framework is applied across systems for supervised training, unsupervised training, and assisted reflection, which reflect the collective work of the Adaptive Mixed Reality Rehabilitation (AMRR) Team at Arizona State University, of which the author is a member. Formal structures and a methodology for applying them are described in detail for the visual media environments designed by the author. Data collected from studies conducted by the AMRR team to evaluate these systems in both supervised and unsupervised training contexts is also discussed in terms of the extent to which the application of the compositional framework is supported and which aspects require further investigation. The potential broader implications of the proposed compositional framework and methodology are the dissemination of interdisciplinary information to accelerate the informed development of INR applications and to demonstrate the potential benefit of generalizing integrative approaches, merging arts and science based knowledge, for other complex problems related to embodied learning. === Dissertation/Thesis === This video shows a demonstration of the home-based AMRR system in use with a stroke survivor. Participant is shown receiving instructions, followed by performance of one interactive Level 1 set of reaching to grasp and lift a portable cylinder. === This video shows a demonstration of the home-based AMRR system in use with a stroke survivor. Participant is shown performing an interactive Level 1 set of reaching to touch a flat object. === This video shows a demonstration of the home-based AMRR system in use with a stroke survivor. Participant is shown performing an interactive Level 2 set of reaching to grasp a cone. === This video shows a demonstration of the home-based AMRR system in use with a stroke survivor. Participant is shown performing an interactive Level 3 set of reaching to grasp and transport the cylinder between two locations. === This video shows a demonstration of the home-based AMRR system in use with a stroke survivor. Participant is shown setting up the targets used for training. === This video shows the clinical AMRR feedback for trajectory performance and speed. Video depicts the image formation and plays the musical phrase generated by a reach that is (1) efficient, (2) with horizontal trajectory deviation, (3) and slow. === This video shows the clinical AMRR feedback for joint function and compensation. Video depicts (1) image rotation for wrist rotation, (2) the orchestral sound for elbow extension, (3) the shoulder compensation sound and (4) the torso compensation sound. === This video shows the clinical AMRR feedback demonstrating different aspects of joint correlation. Video depicts (1) an efficient reach with elbow extension and (2) inefficient reach with shoulder compensation sound. === This video shows the home-based AMRR Level 1 visual feedback for negative vertical trajectory error. Rocks are depicted to have sunken underwater. === This video shows the home-based AMRR Level 1 visual feedback for a successful lift with an efficient reaching path. The clearance of the fog indicates the portable object was lifted beyond a height threshold. === This video shows the home-based AMRR Level 2 visual feedback for overall path and grasp performance. Video depicts feedback for the following types of overall performance: efficient path, curvature, segmentation, and grasp completion. === This video shows the home-based AMRR Level 3 audiovisual feedback for overall efficiency of a sequence task. Video depicts (1) efficient performance, (2) slightly inefficient performance, and (3) severely inefficient performance. === This video shows the home-based AMRR Level 3 audiovisual feedback for overall efficiency of a transport task. Video depicts (1) efficient performance, (2) slightly inefficient performance, and (3) severely inefficient performance. === This video shows a demonstration of the clinical AMRR system in use with a stroke survivor and supervising therapist. Participant is shown performing interactive reaches to a cone. === Doctoral Dissertation Media Arts and Sciences 2014
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