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- All Subjects: Neurorehabilitation
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Description
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.
ContributorsLehrer, Nicole (Author) / Rikakis, Thanassis (Committee member) / Olson, Loren (Committee member) / Wolf, Steven L. (Committee member) / Turaga, Pavan (Committee member) / Arizona State University (Publisher)
Created2014
Description
Startle-evoked-movement (SEM), the involuntary release of a planned movement via a startling stimulus, has gained significant attention recently for its ability to probe motor planning as well as enhance movement of the upper extremity following stroke. We recently showed that hand movements are susceptible to SEM. Interestingly, only coordinated movements of the hand (grasp) but not individuated movements of the finger (finger abduction) were susceptible. It was suggested that this resulted from different neural mechanisms involved in each task; however it is possible this was the result of task familiarity. The objective of this study was to evaluate a more familiar individuated finger movement, typing, to determine if this task was susceptible to SEM. We hypothesized that typing movements will be susceptible to SEM in all fingers. These results indicate that individuated movements of the fingers are susceptible to SEM when the task involves a more familiar task, since the electromyogram (EMG) latency is faster in SCM+ trials compared to SCM- trials. However, the middle finger does not show a difference in terms of the keystroke voltage signal, suggesting the middle finger is less susceptible to SEM. Given that SEM is thought to be mediated by the brainstem, specifically the reticulospinal tract, this suggest that the brainstem may play a role in movements of the distal limb when those movements are very familiar, and the independence of each finger might also have a significant on the effect of SEM. Further research includes understanding SEM in fingers in the stroke population. The implications of this research can impact the way upper extremity rehabilitation is delivered.
ContributorsQuezada Valladares, Maria Jose (Author) / Honeycutt, Claire (Thesis director) / Santello, Marco (Committee member) / Harrington Bioengineering Program (Contributor, Contributor) / Barrett, The Honors College (Contributor)
Created2016-12
Description
Previous research has shown that a loud acoustic stimulus can trigger an individual's prepared movement plan. This movement response is referred to as a startle-evoked movement (SEM). SEM has been observed in the stroke survivor population where results have shown that SEM enhances single joint movements that are usually performed with difficulty. While the presence of SEM in the stroke survivor population advances scientific understanding of movement capabilities following a stroke, published studies using the SEM phenomenon only examined one joint. The ability of SEM to generate multi-jointed movements is understudied and consequently limits SEM as a potential therapy tool. In order to apply SEM as a therapy tool however, the biomechanics of the arm in multi-jointed movement planning and execution must be better understood. Thus, the objective of our study was to evaluate if SEM could elicit multi-joint reaching movements that were accurate in an unrestrained, two-dimensional workspace. Data was collected from ten subjects with no previous neck, arm, or brain injury. Each subject performed a reaching task to five Targets that were equally spaced in a semi-circle to create a two-dimensional workspace. The subject reached to each Target following a sequence of two non-startling acoustic stimuli cues: "Get Ready" and "Go". A loud acoustic stimuli was randomly substituted for the "Go" cue. We hypothesized that SEM is accessible and accurate for unrestricted multi-jointed reaching tasks in a functional workspace and is therefore independent of movement direction. Our results found that SEM is possible in all five Target directions. The probability of evoking SEM and the movement kinematics (i.e. total movement time, linear deviation, average velocity) to each Target are not statistically different. Thus, we conclude that SEM is possible in a functional workspace and is not dependent on where arm stability is maximized. Moreover, coordinated preparation and storage of a multi-jointed movement is indeed possible.
ContributorsOssanna, Meilin Ryan (Author) / Honeycutt, Claire (Thesis director) / Schaefer, Sydney (Committee member) / Harrington Bioengineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2016-12
Description
Popular competitive fighting games such as Super Smash Brothers and Street Fighter have some of the steepest learning curves in the gaming industry. These incredibly technical games require the full attention of the player and often take years to master completely. This barrier of entry prevents newer players from enjoying the competitive social environment that such games offer, creating a rift between casual and competitive players. Learning the rules can sometimes be more difficult than playing the game itself. To truly master these concepts requires personal attention from someone who deeply understands the core mechanics that operate behind the scenes.
Meanwhile, machine learning is growing more advanced by the day. Online retailers like Amazon run complex algorithms to recommend future purchases and monitor price changes. Mobile phones use neural networks to interpret speech. GPS apps track anonymous motion data in smartphones to give real-time traffic estimates. Artificial intelligence is becoming increasingly ubiquitous because of its versatility in analyzing and solving human problems; it follows, then, that a machine could learn how to teach humans skills and techniques. HelperBot is a platform fighting game project that employs this cutting-edge learning technology to close the skill gap between novice and veteran gamers as quickly and seamlessly as possible.
Meanwhile, machine learning is growing more advanced by the day. Online retailers like Amazon run complex algorithms to recommend future purchases and monitor price changes. Mobile phones use neural networks to interpret speech. GPS apps track anonymous motion data in smartphones to give real-time traffic estimates. Artificial intelligence is becoming increasingly ubiquitous because of its versatility in analyzing and solving human problems; it follows, then, that a machine could learn how to teach humans skills and techniques. HelperBot is a platform fighting game project that employs this cutting-edge learning technology to close the skill gap between novice and veteran gamers as quickly and seamlessly as possible.
ContributorsPalermo, Seth Daniel (Author) / Olson, Loren (Thesis director) / Marinelli, Donald (Committee member) / Arts, Media and Engineering Sch T (Contributor) / Mechanical and Aerospace Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2019-05