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- Genre: Doctoral Dissertation
Description
Parents fulfill a pivotal role in early childhood development of social and communication
skills. In children with autism, the development of these skills can be delayed. Applied
behavioral analysis (ABA) techniques have been created to aid in skill acquisition.
Among these, pivotal response treatment (PRT) has been empirically shown to foster
improvements. Research into PRT implementation has also shown that parents can be
trained to be effective interventionists for their children. The current difficulty in PRT
training is how to disseminate training to parents who need it, and how to support and
motivate practitioners after training.
Evaluation of the parents’ fidelity to implementation is often undertaken using video
probes that depict the dyadic interaction occurring between the parent and the child during
PRT sessions. These videos are time consuming for clinicians to process, and often result
in only minimal feedback for the parents. Current trends in technology could be utilized to
alleviate the manual cost of extracting data from the videos, affording greater
opportunities for providing clinician created feedback as well as automated assessments.
The naturalistic context of the video probes along with the dependence on ubiquitous
recording devices creates a difficult scenario for classification tasks. The domain of the
PRT video probes can be expected to have high levels of both aleatory and epistemic
uncertainty. Addressing these challenges requires examination of the multimodal data
along with implementation and evaluation of classification algorithms. This is explored
through the use of a new dataset of PRT videos.
The relationship between the parent and the clinician is important. The clinician can
provide support and help build self-efficacy in addition to providing knowledge and
modeling of treatment procedures. Facilitating this relationship along with automated
feedback not only provides the opportunity to present expert feedback to the parent, but
also allows the clinician to aid in personalizing the classification models. By utilizing a
human-in-the-loop framework, clinicians can aid in addressing the uncertainty in the
classification models by providing additional labeled samples. This will allow the system
to improve classification and provides a person-centered approach to extracting
multimodal data from PRT video probes.
skills. In children with autism, the development of these skills can be delayed. Applied
behavioral analysis (ABA) techniques have been created to aid in skill acquisition.
Among these, pivotal response treatment (PRT) has been empirically shown to foster
improvements. Research into PRT implementation has also shown that parents can be
trained to be effective interventionists for their children. The current difficulty in PRT
training is how to disseminate training to parents who need it, and how to support and
motivate practitioners after training.
Evaluation of the parents’ fidelity to implementation is often undertaken using video
probes that depict the dyadic interaction occurring between the parent and the child during
PRT sessions. These videos are time consuming for clinicians to process, and often result
in only minimal feedback for the parents. Current trends in technology could be utilized to
alleviate the manual cost of extracting data from the videos, affording greater
opportunities for providing clinician created feedback as well as automated assessments.
The naturalistic context of the video probes along with the dependence on ubiquitous
recording devices creates a difficult scenario for classification tasks. The domain of the
PRT video probes can be expected to have high levels of both aleatory and epistemic
uncertainty. Addressing these challenges requires examination of the multimodal data
along with implementation and evaluation of classification algorithms. This is explored
through the use of a new dataset of PRT videos.
The relationship between the parent and the clinician is important. The clinician can
provide support and help build self-efficacy in addition to providing knowledge and
modeling of treatment procedures. Facilitating this relationship along with automated
feedback not only provides the opportunity to present expert feedback to the parent, but
also allows the clinician to aid in personalizing the classification models. By utilizing a
human-in-the-loop framework, clinicians can aid in addressing the uncertainty in the
classification models by providing additional labeled samples. This will allow the system
to improve classification and provides a person-centered approach to extracting
multimodal data from PRT video probes.
ContributorsCopenhaver Heath, Corey D (Author) / Panchanathan, Sethuraman (Thesis advisor) / McDaniel, Troy (Committee member) / Venkateswara, Hemanth (Committee member) / Davulcu, Hasan (Committee member) / Gaffar, Ashraf (Committee member) / Arizona State University (Publisher)
Created2019
Description
Access to real-time situational information including the relative position and motion of surrounding objects is critical for safe and independent travel. Object or obstacle (OO) detection at a distance is primarily a task of the visual system due to the high resolution information the eyes are able to receive from afar. As a sensory organ in particular, the eyes have an unparalleled ability to adjust to varying degrees of light, color, and distance. Therefore, in the case of a non-visual traveler, someone who is blind or low vision, access to visual information is unattainable if it is positioned beyond the reach of the preferred mobility device or outside the path of travel. Although, the area of assistive technology in terms of electronic travel aids (ETA’s) has received considerable attention over the last two decades; surprisingly, the field has seen little work in the area focused on augmenting rather than replacing current non-visual travel techniques, methods, and tools. Consequently, this work describes the design of an intuitive tactile language and series of wearable tactile interfaces (the Haptic Chair, HaptWrap, and HapBack) to deliver real-time spatiotemporal data. The overall intuitiveness of the haptic mappings conveyed through the tactile interfaces are evaluated using a combination of absolute identification accuracy of a series of patterns and subjective feedback through post-experiment surveys. Two types of spatiotemporal representations are considered: static patterns representing object location at a single time instance, and dynamic patterns, added in the HaptWrap, which represent object movement over a time interval. Results support the viability of multi-dimensional haptics applied to the body to yield an intuitive understanding of dynamic interactions occurring around the navigator during travel. Lastly, it is important to point out that the guiding principle of this work centered on providing the navigator with spatial knowledge otherwise unattainable through current mobility techniques, methods, and tools, thus, providing the \emph{navigator} with the information necessary to make informed navigation decisions independently, at a distance.
ContributorsDuarte, Bryan Joiner (Author) / McDaniel, Troy (Thesis advisor) / Davulcu, Hasan (Committee member) / Li, Baoxin (Committee member) / Venkateswara, Hemanth (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