ASU Electronic Theses and Dissertations
This collection includes most of the ASU Theses and Dissertations from 2011 to present. ASU Theses and Dissertations are available in downloadable PDF format; however, a small percentage of items are under embargo. Information about the dissertations/theses includes degree information, committee members, an abstract, supporting data or media.
In addition to the electronic theses found in the ASU Digital Repository, ASU Theses and Dissertations can be found in the ASU Library Catalog.
Dissertations and Theses granted by Arizona State University are archived and made available through a joint effort of the ASU Graduate College and the ASU Libraries. For more information or questions about this collection contact or visit the Digital Repository ETD Library Guide or contact the ASU Graduate College at gradformat@asu.edu.
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- Creators: Davulcu, Hasan
Description
The burden of adaptation has been a major limiting factor in the adoption rates of new wearable assistive technologies. This burden has created a necessity for the exploration and combination of two key concepts in the development of upcoming wearables: anticipation and invisibility. The combination of these two topics has created the field of Anticipatory and Invisible Interfaces (AII)
In this dissertation, a novel framework is introduced for the development of anticipatory devices that augment the proprioceptive system in individuals with neurodegenerative disorders in a seamless way that scaffolds off of existing cognitive feedback models. The framework suggests three main categories of consideration in the development of devices which are anticipatory and invisible:
• Idiosyncratic Design: How do can a design encapsulate the unique characteristics of the individual in the design of assistive aids?
• Adaptation to Intrapersonal Variations: As individuals progress through the various stages of a disability
eurological disorder, how can the technology adapt thresholds for feedback over time to address these shifts in ability?
• Context Aware Invisibility: How can the mechanisms of interaction be modified in order to reduce cognitive load?
The concepts proposed in this framework can be generalized to a broad range of domains; however, there are two primary applications for this work: rehabilitation and assistive aids. In preliminary studies, the framework is applied in the areas of Parkinsonian freezing of gait anticipation and the anticipation of body non-compliance during rehabilitative exercise.
In this dissertation, a novel framework is introduced for the development of anticipatory devices that augment the proprioceptive system in individuals with neurodegenerative disorders in a seamless way that scaffolds off of existing cognitive feedback models. The framework suggests three main categories of consideration in the development of devices which are anticipatory and invisible:
• Idiosyncratic Design: How do can a design encapsulate the unique characteristics of the individual in the design of assistive aids?
• Adaptation to Intrapersonal Variations: As individuals progress through the various stages of a disability
eurological disorder, how can the technology adapt thresholds for feedback over time to address these shifts in ability?
• Context Aware Invisibility: How can the mechanisms of interaction be modified in order to reduce cognitive load?
The concepts proposed in this framework can be generalized to a broad range of domains; however, there are two primary applications for this work: rehabilitation and assistive aids. In preliminary studies, the framework is applied in the areas of Parkinsonian freezing of gait anticipation and the anticipation of body non-compliance during rehabilitative exercise.
ContributorsTadayon, Arash (Author) / Panchanathan, Sethuraman (Thesis advisor) / McDaniel, Troy (Committee member) / Krishnamurthi, Narayanan (Committee member) / Davulcu, Hasan (Committee member) / Li, Baoxin (Committee member) / Arizona State University (Publisher)
Created2020
Description
In videos that contain actions performed unintentionally, agents do not achieve their desired goals. In such videos, it is challenging for computer vision systems to understand high-level concepts such as goal-directed behavior. On the other hand, from a very early age, humans are able to understand the relation between an agent and their ultimate goal even if the action gets disrupted or unintentional effects occur. Inculcating this ability in artificially intelligent agents would make them better social learners by not just learning from their own mistakes, i.e, reinforcement learning, but also learning from other's mistakes. For example, this could greatly reduce the search space for artificially intelligent agents for finding the correct action sequence when trying to achieve a new goal, since they would be able to learn from others what not to do as well as how/when actions result in undesired outcomes.To validate this ability of deep learning models to perform this task, the Weakly Augmented Oops (W-Oops) dataset is proposed, built upon the Oops dataset. W-Oops consists of 2,100 unintentional human action videos, with 44 goal-directed and 33 unintentional video-level activity labels collected through human annotations.
Inspired by previous methods on tasks such as weakly supervised action localization which show promise for achieving good localization results without ground truth segment annotations, this paper proposes a weakly supervised algorithm for localizing the goal-directed as well as the unintentional temporal region of a video using only video-level labels. In particular, an attention mechanism based strategy is employed that predicts the temporal regions which contributes the most to a classification task, leveraging solely video-level labels. Meanwhile, our designed overlap regularization allows the model to focus on distinct portions of the video for inferring the goal-directed and unintentional activity, while guaranteeing their temporal ordering. Extensive quantitative experiments verify the validity of our localization method.
ContributorsChakravarthy, Arnav (Author) / Yang, Yezhou (Thesis advisor) / Davulcu, Hasan (Committee member) / Pavlic, Theodore (Committee member) / Arizona State University (Publisher)
Created2021