Matching Items (13)
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Description
In recent years, machine learning and data mining technologies have received growing attention in several areas such as recommendation systems, natural language processing, speech and handwriting recognition, image processing and biomedical domain. Many of these applications which deal with physiological and biomedical data require person specific or person adaptive systems. The greatest challenge in developing such systems is the subject-dependent data variations or subject-based variability in physiological and biomedical data, which leads to difference in data distributions making the task of modeling these data, using traditional machine learning algorithms, complex and challenging. As a result, despite the wide application of machine learning, efficient deployment of its principles to model real-world data is still a challenge. This dissertation addresses the problem of subject based variability in physiological and biomedical data and proposes person adaptive prediction models based on novel transfer and active learning algorithms, an emerging field in machine learning. One of the significant contributions of this dissertation is a person adaptive method, for early detection of muscle fatigue using Surface Electromyogram signals, based on a new multi-source transfer learning algorithm. This dissertation also proposes a subject-independent algorithm for grading the progression of muscle fatigue from 0 to 1 level in a test subject, during isometric or dynamic contractions, at real-time. Besides subject based variability, biomedical image data also varies due to variations in their imaging techniques, leading to distribution differences between the image databases. Hence a classifier learned on one database may perform poorly on the other database. Another significant contribution of this dissertation has been the design and development of an efficient biomedical image data annotation framework, based on a novel combination of transfer learning and a new batch-mode active learning method, capable of addressing the distribution differences across databases. The methodologies developed in this dissertation are relevant and applicable to a large set of computing problems where there is a high variation of data between subjects or sources, such as face detection, pose detection and speech recognition. From a broader perspective, these frameworks can be viewed as a first step towards design of automated adaptive systems for real world data.
ContributorsChattopadhyay, Rita (Author) / Panchanathan, Sethuraman (Thesis advisor) / Ye, Jieping (Thesis advisor) / Li, Baoxin (Committee member) / Santello, Marco (Committee member) / Arizona State University (Publisher)
Created2013
Description
Situations of sensory overload are steadily becoming more frequent as the ubiquity of technology approaches reality--particularly with the advent of socio-communicative smartphone applications, and pervasive, high speed wireless networks. Although the ease of accessing information has improved our communication effectiveness and efficiency, our visual and auditory modalities--those modalities that today's computerized devices and displays largely engage--have become overloaded, creating possibilities for distractions, delays and high cognitive load; which in turn can lead to a loss of situational awareness, increasing chances for life threatening situations such as texting while driving. Surprisingly, alternative modalities for information delivery have seen little exploration. Touch, in particular, is a promising candidate given that it is our largest sensory organ with impressive spatial and temporal acuity. Although some approaches have been proposed for touch-based information delivery, they are not without limitations including high learning curves, limited applicability and/or limited expression. This is largely due to the lack of a versatile, comprehensive design theory--specifically, a theory that addresses the design of touch-based building blocks for expandable, efficient, rich and robust touch languages that are easy to learn and use. Moreover, beyond design, there is a lack of implementation and evaluation theories for such languages. To overcome these limitations, a unified, theoretical framework, inspired by natural, spoken language, is proposed called Somatic ABC's for Articulating (designing), Building (developing) and Confirming (evaluating) touch-based languages. To evaluate the usefulness of Somatic ABC's, its design, implementation and evaluation theories were applied to create communication languages for two very unique application areas: audio described movies and motor learning. These applications were chosen as they presented opportunities for complementing communication by offloading information, typically conveyed visually and/or aurally, to the skin. For both studies, it was found that Somatic ABC's aided the design, development and evaluation of rich somatic languages with distinct and natural communication units.
ContributorsMcDaniel, Troy Lee (Author) / Panchanathan, Sethuraman (Thesis advisor) / Davulcu, Hasan (Committee member) / Li, Baoxin (Committee member) / Santello, Marco (Committee member) / Arizona State University (Publisher)
Created2012
Description
The generation of walking motion is one of the most vital functions of the human body because it allows us to be mobile in our environment. Unfortunately, numerous individuals suffer from gait impairment as a result of debilitating conditions like stroke, resulting in a serious loss of mobility. Our understanding of human gait is limited by the amount of research we conduct in relation to human walking mechanisms and their characteristics. In order to better understand these characteristics and the systems involved in the generation of human gait, it is necessary to increase the depth and range of research pertaining to walking motion. Specifically, there has been a lack of investigation into a particular area of human gait research that could potentially yield interesting conclusions about gait rehabilitation, which is the effect of surface stiffness on human gait. In order to investigate this idea, a number of studies have been conducted using experimental devices that focus on changing surface stiffness; however, these systems lack certain functionality that would be useful in an experimental scenario. To solve this problem and to investigate the effect of surface stiffness further, a system has been developed called the Variable Stiffness Treadmill system (VST). This treadmill system is a unique investigative tool that allows for the active control of surface stiffness. What is novel about this system is its ability to change the stiffness of the surface quickly, accurately, during the gait cycle, and throughout a large range of possible stiffness values. This type of functionality in an experimental system has never been implemented and constitutes a tremendous opportunity for valuable gait research in regard to the influence of surface stiffness. In this work, the design, development, and implementation of the Variable Stiffness Treadmill system is presented and discussed along with preliminary experimentation. The results from characterization testing demonstrate highly accurate stiffness control and excellent response characteristics for specific configurations. Initial indications from human experimental trials in relation to quantifiable effects from surface stiffness variation using the Variable Stiffness Treadmill system are encouraging.
ContributorsBarkan, Andrew Robert (Author) / Artemiadis, Panagiotis (Thesis director) / Santello, Marco (Committee member) / Barrett, The Honors College (Contributor) / Mechanical and Aerospace Engineering Program (Contributor)
Created2015-05
Description
Most daily living tasks consist of pairing a series of sequential movements, e.g., reaching to a cup, grabbing the cup, lifting and returning the cup to your mouth. The process by which we control and mediate the smooth progression of these tasks is not well understood. One method which we can use to further evaluate these motions is known as Startle Evoked Movements (SEM). SEM is an established technique to probe the motor learning and planning processes by detecting muscle activation of the sternocleidomastoid muscles of the neck prior to 120ms after a startling stimulus is presented. If activation of these muscles was detected following a stimulus in the 120ms window, the movement is classified as Startle+ whereas if no sternocleidomastoid activation is detected after a stimulus in the allotted time the movement is considered Startle-. For a movement to be considered SEM, the activation of movements for Startle+ trials must be faster than the activation of Startle- trials. The objective of this study was to evaluate the effect that expertise has on sequential movements as well as determining if startle can distinguish when the consolidation of actions, known as chunking, has occurred. We hypothesized that SEM could distinguish words that were solidified or chunked. Specifically, SEM would be present when expert typists were asked to type a common word but not during uncommon letter combinations. The results from this study indicated that the only word that was susceptible to SEM, where Startle+ trials were initiated faster than Startle-, was an uncommon task "HET" while the common words "AND" and "THE" were not. Additionally, the evaluation of the differences between each keystroke for common and uncommon words showed that Startle was unable to distinguish differences in motor chunking between Startle+ and Startle- trials. Explanations into why these results were observed could be related to hand dominance in expert typists. No proper research has been conducted to evaluate the susceptibility of the non-dominant hand's fingers to SEM, and the results of future studies into this as well as the results from this study can impact our understanding of sequential movements.
ContributorsMieth, Justin Richard (Author) / Honeycutt, Claire (Thesis director) / Santello, Marco (Committee member) / Harrington Bioengineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2018-05
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
In the last 15 years, there has been a significant increase in the number of motor neural prostheses used for restoring limb function lost due to neurological disorders or accidents. The aim of this technology is to enable patients to control a motor prosthesis using their residual neural pathways (central or peripheral). Recent studies in non-human primates and humans have shown the possibility of controlling a prosthesis for accomplishing varied tasks such as self-feeding, typing, reaching, grasping, and performing fine dexterous movements. A neural decoding system comprises mainly of three components: (i) sensors to record neural signals, (ii) an algorithm to map neural recordings to upper limb kinematics and (iii) a prosthetic arm actuated by control signals generated by the algorithm. Machine learning algorithms that map input neural activity to the output kinematics (like finger trajectory) form the core of the neural decoding system. The choice of the algorithm is thus, mainly imposed by the neural signal of interest and the output parameter being decoded. The various parts of a neural decoding system are neural data, feature extraction, feature selection, and machine learning algorithm. There have been significant advances in the field of neural prosthetic applications. But there are challenges for translating a neural prosthesis from a laboratory setting to a clinical environment. To achieve a fully functional prosthetic device with maximum user compliance and acceptance, these factors need to be addressed and taken into consideration. Three challenges in developing robust neural decoding systems were addressed by exploring neural variability in the peripheral nervous system for dexterous finger movements, feature selection methods based on clinically relevant metrics and a novel method for decoding dexterous finger movements based on ensemble methods.
ContributorsPadmanaban, Subash (Author) / Greger, Bradley (Thesis advisor) / Santello, Marco (Committee member) / Helms Tillery, Stephen (Committee member) / Papandreou-Suppappola, Antonia (Committee member) / Crook, Sharon (Committee member) / Arizona State University (Publisher)
Created2017
Description
Repetitive practice of functional movement patterns during motor rehabilitation are known to drive learning (or relearning) of novel motor skills, but the learning process is highly variable between individuals such that responsiveness to task-specific training is often patient-specific. A number of neuroimaging and neurophysiological methods have been proposed to better predict a patient’s responsiveness to a given type or dose of motor therapy. However, these methods are often time- and resource-intensive, and yield results that are not readily interpretable by clinicians. In contrast, standardized visuospatial tests may offer a more feasible solution. The work presented in this dissertation demonstrate that a clinical paper-and-pencil test of visuospatial function may improve predictive models of motor skill learning in older adults and individuals with stroke pathology. To further our understanding of the neuroanatomical correlates underlying this behavioral relationship, I collected diffusion-weighted magnetic resonance images from 19 nondemented older adults to determine if diffusion characteristics of white matter tracts explain shared variance in delayed visuospatial memory test scores and motor skill learning. Consistent with previous work, results indicated that the structural integrity of regions with the bilateral anterior thalamic radiations, corticospinal tracts, and superior longitudinal fasciculi are related to delayed visuospatial memory performance and one-week skill retention. Overall, results of this dissertation suggest that incorporating a clinical paper-and-pencil test of delayed visuospatial memory may prognose motor rehabilitation outcomes and support that personalized variables should be considered in standards of care. Moreover, regions within specific white matter tracts may underlie this behavioral relationship and future work should investigate these regions as potential targets for therapeutic intervention.
ContributorsLingo VanGilder, Jennapher (Author) / Schaefer, Sydney Y (Thesis advisor) / Santello, Marco (Committee member) / Buneo, Christopher (Committee member) / Rogalsky, Corianne (Committee member) / Duff, Kevin (Committee member) / Arizona State University (Publisher)
Created2021
Description
The human hand comprises complex sensorimotor functions that can be impaired by neurological diseases and traumatic injuries. Effective rehabilitation can bring the impaired hand back to a functional state because of the plasticity of the central nervous system to relearn and remodel the lost synapses in the brain. Current rehabilitation therapies focus on strengthening motor skills, such as grasping, employ multiple objects of varying stiffness and devices that are bulky, costly, and have limited range of stiffness due to the rigid mechanisms employed in their variable stiffness actuators. This research project presents a portable cost-effective soft robotic haptic device with a broad stiffness range that is adjustable and can be utilized in both clinical and home settings. The device eliminates the need for multiple objects by employing a pneumatic soft structure made with highly compliant materials that act as the actuator as well as the structure of the haptic interface. It is made with interchangeable soft elastomeric sleeves that can be customized to include materials of varying stiffness to increase or decrease the stiffness range. The device is fabricated using existing 3D printing technologies, and polymer molding and casting techniques, thus keeping the cost low and throughput high. The haptic interface is linked to either an open-loop system that allows for an increased pressure during usage or closed-loop system that provides pressure regulation in accordance with the stiffness the user specifies. A preliminary evaluation is performed to characterize the effective controllable region of variance in stiffness. Results indicate that the region of controllable stiffness was in the center of the device, where the stiffness appeared to plateau with each increase in pressure. The two control systems are tested to derive relationships between internal pressure, grasping force exertion on the surface, and displacement using multiple probing points on the haptic device. Additional quantitative evaluation is performed with study participants and juxtaposed to a qualitative analysis to ensure adequate perception in compliance variance. Finally, a qualitative evaluation showed that greater than 60% of the trials resulted in the correct perception of stiffness in the haptic device.
ContributorsSebastian, Frederick (Author) / Polygerinos, Panagiotis (Thesis advisor) / Santello, Marco (Committee member) / Fu, Qiushi (Committee member) / Arizona State University (Publisher)
Created2018
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