Matching Items (12)
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- All Subjects: deep learning
- All Subjects: Rehabilitation
- Creators: McDaniel, Troy
Description
This paper presents work that was done to create a system capable of facial expression recognition (FER) using deep convolutional neural networks (CNNs) and test multiple configurations and methods. CNNs are able to extract powerful information about an image using multiple layers of generic feature detectors. The extracted information can be used to understand the image better through recognizing different features present within the image. Deep CNNs, however, require training sets that can be larger than a million pictures in order to fine tune their feature detectors. For the case of facial expression datasets, none of these large datasets are available. Due to this limited availability of data required to train a new CNN, the idea of using naïve domain adaptation is explored. Instead of creating and using a new CNN trained specifically to extract features related to FER, a previously trained CNN originally trained for another computer vision task is used. Work for this research involved creating a system that can run a CNN, can extract feature vectors from the CNN, and can classify these extracted features. Once this system was built, different aspects of the system were tested and tuned. These aspects include the pre-trained CNN that was used, the layer from which features were extracted, normalization used on input images, and training data for the classifier. Once properly tuned, the created system returned results more accurate than previous attempts on facial expression recognition. Based on these positive results, naïve domain adaptation is shown to successfully leverage advantages of deep CNNs for facial expression recognition.
ContributorsEusebio, Jose Miguel Ang (Author) / Panchanathan, Sethuraman (Thesis director) / McDaniel, Troy (Committee member) / Venkateswara, Hemanth (Committee member) / Computer Science and Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2016-05
Description
This paper presents the design and evaluation of a haptic interface for augmenting human-human interpersonal interactions by delivering facial expressions of an interaction partner to an individual who is blind using a visual-to-tactile mapping of facial action units and emotions. Pancake shaftless vibration motors are mounted on the back of a chair to provide vibrotactile stimulation in the context of a dyadic (one-on-one) interaction across a table. This work explores the design of spatiotemporal vibration patterns that can be used to convey the basic building blocks of facial movements according to the Facial Action Unit Coding System. A behavioral study was conducted to explore the factors that influence the naturalness of conveying affect using vibrotactile cues.
ContributorsBala, Shantanu (Author) / Panchanathan, Sethuraman (Thesis director) / McDaniel, Troy (Committee member) / Barrett, The Honors College (Contributor) / Computer Science and Engineering Program (Contributor) / Department of Psychology (Contributor)
Created2014-05
Description
Skin and muscle receptors in the leg and foot provide able-bodied humans with force and position information that is crucial for balance and movement control. In lower-limb amputees however, this vital information is either missing or incomplete. Amputees typically compensate for the loss of sensory information by relying on haptic feedback from the stump-socket interface. Unfortunately, this is not an adequate substitute. Areas of the stump that directly interface with the socket are also prone to painful irritation, which further degrades haptic feedback. The lack of somatosensory feedback from prosthetic legs causes several problems for lower-limb amputees. Previous studies have established that the lack of adequate sensory feedback from prosthetic limbs contributes to poor balance and abnormal gait kinematics. These improper gait kinematics can, in turn, lead to the development of musculoskeletal diseases. Finally, the absence of sensory information has been shown to lead to steeper learning curves and increased rehabilitation times, which hampers amputees from recovering from the trauma. In this study, a novel haptic feedback system for lower-limb amputees was develped, and studies were performed to verify that information presented was sufficiently accurate and precise in comparison to a Bertec 4060-NC force plate. The prototype device consisted of a sensorized insole, a belt-mounted microcontroller, and a linear array of four vibrotactile motors worn on the thigh. The prototype worked by calculating the center of pressure in the anteroposterior plane, and applying a time-discrete vibrotactile stimulus based on the location of the center of pressure.
ContributorsKaplan, Gabriel Benjamin (Author) / Abbas, James (Thesis director) / McDaniel, Troy (Committee member) / School of Life Sciences (Contributor) / Barrett, The Honors College (Contributor)
Created2018-05
Description
This paper presents a system to deliver automated, noninvasive, and effective fine motor rehabilitation through a rhythm-based game using a Leap Motion Controller. The system is a rhythm game where hand gestures are used as input and must match the rhythm and gestures shown on screen, thus allowing a physical therapist to represent an exercise session involving the user's hand and finger joints as a series of patterns. Fine motor rehabilitation plays an important role in the recovery and improvement of the effects of stroke, Parkinson's disease, multiple sclerosis, and more. Individuals with these conditions possess a wide range of impairment in terms of fine motor movement. The serious game developed takes this into account and is designed to work with individuals with different levels of impairment. In a pilot study, under partnership with South West Advanced Neurological Rehabilitation (SWAN Rehab) in Phoenix, Arizona, we compared the performance of individuals with fine motor impairment to individuals without this impairment to determine whether a human-centered approach and adapting to an user's range of motion can allow an individual with fine motor impairment to perform at a similar level as a non-impaired user.
ContributorsShah, Vatsal Nimishkumar (Author) / McDaniel, Troy (Thesis director) / Tadayon, Ramin (Committee member) / Computer Science and Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2018-05
Description
This thesis work presents two separate studies:The first study assesses standing balance under various 2-dimensional (2D) compliant environments simulated using a dual-axis robotic platform and vision conditions. Directional virtual time-to-contact (VTC) measures were introduced to better characterize postural balance from both temporal and spatial aspects, and enable prediction of fall-relevant directions. Twenty healthy young adults were recruited to perform quiet standing tasks on the platform. Conventional stability measures, namely center-of-pressure (COP) path length and COP area, were also adopted for further comparisons with the proposed VTC. The results indicated that postural balance was adversely impacted, evidenced by significant decreases in VTC and increases in COP path length/area measures, as the ground compliance increased and/or in the absence of vision (ps < 0.001). Interaction effects between environment and vision were observed in VTC and COP path length measures (ps ≤ 0.05), but not COP area (p = 0.103). The estimated likelihood of falls in anterior-posterior (AP) and medio-lateral (ML) directions converged to nearly 50% (almost independent of the foot setting) as the experimental condition became significantly challenging.
The second study introduces a deep learning approach using convolutional neural network (CNN) for predicting environments based on instant observations of sway during balance tasks. COP data were collected from fourteen subjects while standing on the 2D compliant environments. Different window sizes for data segmentation were examined to identify its minimal length for reliable prediction. Commonly-used machine learning models were also tested to compare their effectiveness with that of the presented CNN model. The CNN achieved above 94.5% in the overall prediction accuracy even with 2.5-second length data, which cannot be achieved by traditional machine learning models (ps < 0.05). Increasing data length beyond 2.5 seconds slightly improved the accuracy of CNN but substantially increased training time (60% longer). Importantly, averaged normalized confusion matrices revealed that CNN is much more capable of differentiating the mid-level environmental condition.
These two studies provide new perspectives in human postural balance, which cannot be
interpreted by conventional stability analyses. Outcomes of these studies contribute to the advancement of human interactive robots/devices for fall prevention and rehabilitation.
ContributorsPhan, Vu Nguyen (Author) / Lee, Hyunglae (Thesis advisor) / Peterson, Daniel (Committee member) / Marvi, Hamidreza (Committee member) / Arizona State University (Publisher)
Created2021
Description
The impact of Artificial Intelligence (AI) has increased significantly in daily life. AI is taking big strides towards moving into areas of life that are critical such as
healthcare but, also into areas such as entertainment and leisure. Deep neural
networks have been pivotal in making all these advancements possible. But, a well-known problem with deep neural networks is the lack of explanations for the choices
it makes. To combat this, several methods have been tried in the field of research.
One example of this is assigning rankings to the individual features and how influential
they are in the decision-making process. In contrast a newer class of methods focuses
on Concept Activation Vectors (CAV) which focus on extracting higher-level concepts
from the trained model to capture more information as a mixture of several features
and not just one. The goal of this thesis is to employ concepts in a novel domain: to
explain how a deep learning model uses computer vision to classify music into different
genres. Due to the advances in the field of computer vision with deep learning for
classification tasks, it is rather a standard practice now to convert an audio clip into
corresponding spectrograms and use those spectrograms as image inputs to the deep
learning model. Thus, a pre-trained model can classify the spectrogram images
(representing songs) into musical genres. The proposed explanation system called
“Why Pop?” tries to answer certain questions about the classification process such as
what parts of the spectrogram influence the model the most, what concepts were
extracted and how are they different for different classes. These explanations aid the
user gain insights into the model’s learnings, biases, and the decision-making process.
ContributorsSharma, Shubham (Author) / Bryan, Chris (Thesis advisor) / McDaniel, Troy (Committee member) / Sarwat, Mohamed (Committee member) / Arizona State University (Publisher)
Created2022
Description
The genetic disorder Down syndrome (DS), clinically known as Trisomy 21, is characterized by the presence of either a part or full extra copy of chromosome 21. When compared with children of typical development, children with DS consistently score lower on gross motor skill tasks. Balance specifically is one of the hardest skills for individuals with DS (especially
children) to acquire, and neglecting to train balance early on can predispose individuals with DS to further movement instabilities, injuries, social struggles from activity limitations, and an overall lack of independence. One of the more unique forms of physical activity that requires a large amount of both static and dynamic balance is ballet. Dance-specific therapy has been shown to improve gross motor control functioning and specifically balance in a variety of populations with neuromuscular condions, but the research around ballet-specific therapy for those with DS is lacking. The purpose of this pilot study was to further investigate the effects of ballet-specific training on balance ability and general motor functioning in young students with DS as measured by the Four Square Step Test (FSST), Pediatric Balance Scale (PBS), and the gross and fine motor domains of the Vineland Adaptive Behavior Scale lll (VABS lll). It was hypothesized that participation in the 6-week summer cohort of Ballet Arizona’s Adaptive Dance Program would lead to improved scores on the PBS and VABS lll and decreased test times in the FSST. Improvements were observed for all measures for both participants (sample size n=2), except for P1's FSST, which increased in post-testing by 2.25s. Due to the study design, no conclusive statements can be made about whether the ballet program was responsible for the improvements observed in post-testing. More rigorous research with larger sample sizes (>30) is warranted to more fully understand the impact of an adapted ballet program on the balance ability of young individuals with DS. However, the program is still recommended for young individuals with DS because of the benefits it provides outside of motor skill development.
ContributorsMitrovic, Jelena (Author) / Peterson, Daniel (Thesis director) / Holzapfel, Simon (Committee member) / Barrett, The Honors College (Contributor) / College of Health Solutions (Contributor)
Created2022-12
Description
One of the long-standing issues that has arisen in the sports medicine field is identifying the ideal methodology to optimize recovery following anterior cruciate ligament reconstruction (ACLR). The perioperative period for ACLR is notoriously heterogeneous in nature as it consists of many variables that can impact surgical outcomes. While there has been extensive literature published regarding the efficacy of various recovery and rehabilitation topics, it has been widely acknowledged that certain modalities within the field of ACLR rehabilitation need further high-quality evidence to support their use in clinical practice, such as blood flow restriction (BFR) training. BFR training involves the application of a tourniquet-like cuff to the proximal aspect of a limb prior to exercise; the cuff is inflated so that it occludes venous flow but allows arterial inflow. BFR is usually combined with low-intensity (LI) resistance training, with resistance as low as 20% of one-repetition maximum (1RM). LI-BFR has been used as an emerging clinical modality to combat postoperative atrophy of the quadriceps muscles for those who have undergone ACLR, as these individuals cannot safely tolerate high muscular tension exercise after surgery. Impairments of the quadriceps are the major cause of poor functional status of patients following an otherwise successful ACLR procedure; however, these impairments can be mitigated with preoperative rehabilitation done before surgery. It was hypothesized that the use of a preoperative LI-BFR training protocol could help improve postoperative outcomes following ACLR; primarily, strength and hypertrophy of the quadriceps. When compared with a SHAM control group, subjects who were randomized to a BFR intervention group made greater preoperative strength gains in the quadriceps and recovered quadriceps mass at an earlier timepoint than that of the SHAM group aftersurgery; however, the gains made in strength were not able to be maintained in the 8-week postoperative period. While these results do not support the use of LI-BFR from the short-term perspective after ACLR, follow-up data will be used to investigate trends in re-injury and return to sport rates to evaluate the efficacy of the use of LI-BFR from a long-term perspective.
ContributorsGlattke, Kaycee Elizabeth (Author) / Lockhart, Thurmon (Thesis advisor) / McDaniel, Troy (Committee member) / Banks, Scott (Committee member) / Peterson, Daniel (Committee member) / Lee, Hyunglae (Committee member) / Arizona State University (Publisher)
Created2022
Description
With an aging population, the number of later in life health related incidents like stroke stand to become more prevalent. Unfortunately, the majority those who are most at risk for debilitating heath episodes are either uninsured or under insured when it comes to long term physical/occupational therapy. As insurance companies lower coverage and/or raise prices of plans with sufficient coverage, it can be expected that the proportion of uninsured/under insured to fully insured people will rise. To address this, lower cost alternative methods of treatment must be developed so people can obtain the treated required for a sufficient recovery. The presented robotic glove employs low cost fabric soft pneumatic actuators which use a closed loop feedback controller based on readings from embedded soft sensors. This provides the device with proprioceptive abilities for the dynamic control of each independent actuator. Force and fatigue tests were performed to determine the viability of the actuator design. A Box and Block test along with a motion capture study was completed to study the performance of the device. This paper presents the design and classification of a soft robotic glove with a feedback controller as a at-home stroke rehabilitation device.
ContributorsAxman, Reed C (Author) / Zhang, Wenlong (Thesis advisor) / Santello, Marco (Committee member) / McDaniel, Troy (Committee member) / Arizona State University (Publisher)
Created2022
Description
Over the past decade, advancements in neural networks have been instrumental in achieving remarkable breakthroughs in the field of computer vision. One of the applications is in creating assistive technology to improve the lives of visually impaired people by making the world around them more accessible. A lot of research in convolutional neural networks has led to human-level performance in different vision tasks including image classification, object detection, instance segmentation, semantic segmentation, panoptic segmentation and scene text recognition. All the before mentioned tasks, individually or in combination, have been used to create assistive technologies to improve accessibility for the blind.
This dissertation outlines various applications to improve accessibility and independence for visually impaired people during shopping by helping them identify products in retail stores. The dissertation includes the following contributions; (i) A dataset containing images of breakfast-cereal products and a classifier using a deep neural (ResNet) network; (ii) A dataset for training a text detection and scene-text recognition model; (iii) A model for text detection and scene-text recognition to identify product images using a user-controlled camera; (iv) A dataset of twenty thousand products with product information and related images that can be used to train and test a system designed to identify products.
This dissertation outlines various applications to improve accessibility and independence for visually impaired people during shopping by helping them identify products in retail stores. The dissertation includes the following contributions; (i) A dataset containing images of breakfast-cereal products and a classifier using a deep neural (ResNet) network; (ii) A dataset for training a text detection and scene-text recognition model; (iii) A model for text detection and scene-text recognition to identify product images using a user-controlled camera; (iv) A dataset of twenty thousand products with product information and related images that can be used to train and test a system designed to identify products.
ContributorsPatel, Akshar (Author) / Panchanathan, Sethuraman (Thesis advisor) / Venkateswara, Hemanth (Thesis advisor) / McDaniel, Troy (Committee member) / Arizona State University (Publisher)
Created2020