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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 the last decade deep learning based models have revolutionized machine learning and computer vision applications. However, these models are data-hungry and training them is a time-consuming process. In addition, when deep neural networks are updated to augment their prediction space with new data, they run into the problem of catastrophic forgetting, where the model forgets previously learned knowledge as it overfits to the newly available data. Incremental learning algorithms enable deep neural networks to prevent catastrophic forgetting by retaining knowledge of previously observed data while also learning from newly available data.
This thesis presents three models for incremental learning; (i) Design of an algorithm for generative incremental learning using a pre-trained deep neural network classifier; (ii) Development of a hashing based clustering algorithm for efficient incremental learning; (iii) Design of a student-teacher coupled neural network to distill knowledge for incremental learning. The proposed algorithms were evaluated using popular vision datasets for classification tasks. The thesis concludes with a discussion about the feasibility of using these techniques to transfer information between networks and also for incremental learning applications.
This thesis presents three models for incremental learning; (i) Design of an algorithm for generative incremental learning using a pre-trained deep neural network classifier; (ii) Development of a hashing based clustering algorithm for efficient incremental learning; (iii) Design of a student-teacher coupled neural network to distill knowledge for incremental learning. The proposed algorithms were evaluated using popular vision datasets for classification tasks. The thesis concludes with a discussion about the feasibility of using these techniques to transfer information between networks and also for incremental learning applications.
ContributorsPatil, Rishabh (Author) / Venkateswara, Hemanth (Thesis advisor) / Panchanathan, Sethuraman (Thesis advisor) / McDaniel, Troy (Committee member) / Arizona State University (Publisher)
Created2020
Description
Humans have a great ability to recognize objects in different environments irrespective of their variations. However, the same does not apply to machine learning models which are unable to generalize to images of objects from different domains. The generalization of these models to new data is constrained by the domain gap. Many factors such as image background, image resolution, color, camera perspective and variations in the objects are responsible for the domain gap between the training data (source domain) and testing data (target domain). Domain adaptation algorithms aim to overcome the domain gap between the source and target domains and learn robust models that can perform well across both the domains.
This thesis provides solutions for the standard problem of unsupervised domain adaptation (UDA) and the more generic problem of generalized domain adaptation (GDA). The contributions of this thesis are as follows. (1) Certain and Consistent Domain Adaptation model for closed-set unsupervised domain adaptation by aligning the features of the source and target domain using deep neural networks. (2) A multi-adversarial deep learning model for generalized domain adaptation. (3) A gating model that detects out-of-distribution samples for generalized domain adaptation.
The models were tested across multiple computer vision datasets for domain adaptation.
The dissertation concludes with a discussion on the proposed approaches and future directions for research in closed set and generalized domain adaptation.
This thesis provides solutions for the standard problem of unsupervised domain adaptation (UDA) and the more generic problem of generalized domain adaptation (GDA). The contributions of this thesis are as follows. (1) Certain and Consistent Domain Adaptation model for closed-set unsupervised domain adaptation by aligning the features of the source and target domain using deep neural networks. (2) A multi-adversarial deep learning model for generalized domain adaptation. (3) A gating model that detects out-of-distribution samples for generalized domain adaptation.
The models were tested across multiple computer vision datasets for domain adaptation.
The dissertation concludes with a discussion on the proposed approaches and future directions for research in closed set and generalized domain adaptation.
ContributorsNagabandi, Bhadrinath (Author) / Panchanathan, Sethuraman (Thesis advisor) / Venkateswara, Hemanth (Thesis advisor) / McDaniel, Troy (Committee member) / Arizona State University (Publisher)
Created2020
Description
Societal infrastructure is built with vision at the forefront of daily life. For those with
severe visual impairments, this creates countless barriers to the participation and
enjoyment of life’s opportunities. Technological progress has been both a blessing and
a curse in this regard. Digital text together with screen readers and refreshable Braille
displays have made whole libraries readily accessible and rideshare tech has made
independent mobility more attainable. Simultaneously, screen-based interactions and
experiences have only grown in pervasiveness and importance, precluding many of
those with visual impairments.
Sensory Substituion, the process of substituting an unavailable modality with
another one, has shown promise as an alternative to accomodation, but in recent
years meaningful strides in Sensory Substitution for vision have declined in frequency.
Given recent advances in Computer Vision, this stagnation is especially disconcerting.
Designing Sensory Substitution Devices (SSDs) for vision for use in interactive settings
that leverage modern Computer Vision techniques presents a variety of challenges
including perceptual bandwidth, human-computer-interaction, and person-centered
machine learning considerations. To surmount these barriers an approach called Per-
sonal Foveated Haptic Gaze (PFHG), is introduced. PFHG consists of two primary
components: a human visual system inspired interaction paradigm that is intuitive
and flexible enough to generalize to a variety of applications called Foveated Haptic
Gaze (FHG), and a person-centered learning component to address the expressivity
limitations of most SSDs. This component is called One-Shot Object Detection by
Data Augmentation (1SODDA), a one-shot object detection approach that allows a
user to specify the objects they are interested in locating visually and with minimal
effort realizing an object detection model that does so effectively.
The Personal Foveated Haptic Gaze framework was realized in a virtual and real-
world application: playing a 3D, interactive, first person video game (DOOM) and
finding user-specified real-world objects. User study results found Foveated Haptic
Gaze to be an effective and intuitive interface for interacting with dynamic visual
world using solely haptics. Additionally, 1SODDA achieves competitive performance
among few-shot object detection methods and high-framerate many-shot object de-
tectors. The combination of which paves the way for modern Sensory Substitution
Devices for vision.
severe visual impairments, this creates countless barriers to the participation and
enjoyment of life’s opportunities. Technological progress has been both a blessing and
a curse in this regard. Digital text together with screen readers and refreshable Braille
displays have made whole libraries readily accessible and rideshare tech has made
independent mobility more attainable. Simultaneously, screen-based interactions and
experiences have only grown in pervasiveness and importance, precluding many of
those with visual impairments.
Sensory Substituion, the process of substituting an unavailable modality with
another one, has shown promise as an alternative to accomodation, but in recent
years meaningful strides in Sensory Substitution for vision have declined in frequency.
Given recent advances in Computer Vision, this stagnation is especially disconcerting.
Designing Sensory Substitution Devices (SSDs) for vision for use in interactive settings
that leverage modern Computer Vision techniques presents a variety of challenges
including perceptual bandwidth, human-computer-interaction, and person-centered
machine learning considerations. To surmount these barriers an approach called Per-
sonal Foveated Haptic Gaze (PFHG), is introduced. PFHG consists of two primary
components: a human visual system inspired interaction paradigm that is intuitive
and flexible enough to generalize to a variety of applications called Foveated Haptic
Gaze (FHG), and a person-centered learning component to address the expressivity
limitations of most SSDs. This component is called One-Shot Object Detection by
Data Augmentation (1SODDA), a one-shot object detection approach that allows a
user to specify the objects they are interested in locating visually and with minimal
effort realizing an object detection model that does so effectively.
The Personal Foveated Haptic Gaze framework was realized in a virtual and real-
world application: playing a 3D, interactive, first person video game (DOOM) and
finding user-specified real-world objects. User study results found Foveated Haptic
Gaze to be an effective and intuitive interface for interacting with dynamic visual
world using solely haptics. Additionally, 1SODDA achieves competitive performance
among few-shot object detection methods and high-framerate many-shot object de-
tectors. The combination of which paves the way for modern Sensory Substitution
Devices for vision.
ContributorsFakhri, Bijan (Author) / Panchanathan, Sethuraman (Thesis advisor) / McDaniel, Troy L (Committee member) / Venkateswara, Hemanth (Committee member) / Amor, Heni (Committee member) / Arizona State University (Publisher)
Created2020
Description
Individuals with voice disorders experience challenges communicating daily. These challenges lead to a significant decrease in the quality of life for individuals with dysphonia. While voice amplification systems are often employed as a voice-assistive technology, individuals with voice disorders generally still experience difficulties being understood while using voice amplification systems. With the goal of developing systems that help improve the quality of life of individuals with dysphonia, this work outlines the landscape of voice-assistive technology, the inaccessibility of state-of-the-art voice-based technology and the need for the development of intelligibility improving voice-assistive technologies designed both with and for individuals with voice disorders. With the rise of voice-based technologies in society, in order for everyone to participate in the use of voice-based technologies individuals with voice disorders must be included in both the data that is used to train these systems and the design process. An important and necessary step towards the development of better voice assistive technology as well as more inclusive voice-based systems is the creation of a large, publicly available dataset of dysphonic speech. To this end, a web-based platform to crowdsource voice disorder speech was developed to create such a dataset. This dataset will be released so that it is freely and publicly available to stimulate research in the field of voice-assistive technologies. Future work includes building a robust intelligibility estimation model, as well as employing that model to measure, and therefore enhance, the intelligibility of a given utterance. The hope is that this model will lead to the development of voice-assistive technology using state-of-the-art machine learning models to help individuals with voice disorders be better understood.
ContributorsMoore, Meredith Kay (Author) / Panchanathan, Sethuraman (Thesis advisor) / Berisha, Visar (Committee member) / McDaniel, Troy (Committee member) / Venkateswara, Hemanth (Committee member) / Arizona State University (Publisher)
Created2020