Matching Items (140)
Description
Approximately 1.7 million people in the United States are living with limb loss and are in need of more sophisticated devices that better mimic human function. In the Human Machine Integration Laboratory, a powered, transtibial prosthetic ankle was designed and build that allows a person to regain ankle function with improved ankle kinematics and kinetics. The ankle allows a person to walk normally and up and down stairs, but volitional control is still an issue. This research tackled the problem of giving the user more control over the prosthetic ankle using a force/torque circuit. When the user presses against a force/torque sensor located inside the socket the prosthetic foot plantar flexes or moves downward. This will help the user add additional push-off force when walking up slopes or stairs. It also gives the user a sense of control over the device.
ContributorsFronczyk, Adam (Author) / Sugar, Thomas G. (Thesis advisor) / Helms-Tillery, Stephen (Thesis advisor) / 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
The primary motor cortex (M1) plays a vital role in motor planning and execution, as well as in motor learning. Baseline corticospinal excitability (CSE) in M1 is known to increase as a result of motor learning, but less is understand about the modulation of CSE at the pre-execution planning stage due to learning. This question was addressed using single pulse transcranial magnetic stimulation (TMS) to measure the modulation of both baseline and planning CSE due to learning a reach to grasp task. It was hypothesized that baseline CSE would increase and planning CSE decrease as a function of trial; an increase in baseline CSE would replicate established findings in the literature, while a decrease in planning would be a novel finding. Eight right-handed subjects were visually cued to exert a precise grip force, with the goal of producing that force accurately and consistently. Subjects effectively learned the task in the first 10 trials, but no significant trends were found in the modulation of baseline or planning CSE. The lack of significant results may be due to the very quick learning phase or the lower intensity of training as compared to past studies. The findings presented here suggest that planning and baseline CSE may be modulated along different time courses as learning occurs and point to some important considerations for future studies addressing this question.
ContributorsMoore, Dalton Dale (Author) / Santello, Marco (Thesis director) / Kleim, Jeff (Committee member) / Barrett, The Honors College (Contributor) / Harrington Bioengineering Program (Contributor)
Created2015-05
Description
The intervertebral disc goes through degenerative changes with age, which leads to disc thinning, bulging, or herniation. Spinal fusion treatments are ineffective as they cause quicker degeneration of adjacent discs and fail in nearly 20% of cases, so researchers have turned to tissue-engineering biocompatible intervertebral discs for transplantation. However novel and effective as this may seem, these transplanted discs still show evidence of degeneration after just 5 years. I hypothesize that these discs are degenerating due to a blockage of the cartilaginous endplates post-transplantation that is hindering nutrient transport through the intervertebral disc. In order to test this hypothesis, I developed a mathematical model of nutrient transport through the intervertebral disc in one diurnal daily loading cycle. This model was used to simulate open endplates and blocked endplates and then compare differences in nutrient concentration and nutrient transport to the center of the disc. Results from the math model simulations were then compared to in vitro experimental data collected in lab to verify the findings on a physiological level. Results showed significant differences, both in vitro and in the model, between nutrient transport in open endplates vs blocked endplates, lending support to the original hypothesis. This study only presents preliminary results, but could hold the key to preventing future disc degeneration post-transplantation.
ContributorsMunter, Bryce Taylor (Author) / Santello, Marco (Thesis director) / Caplan, Michael (Committee member) / Giers, Morgan (Committee member) / Barrett, The Honors College (Contributor) / Harrington Bioengineering Program (Contributor)
Created2015-05
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
The artificial neural network is a form of machine learning that is highly effective at recognizing patterns in large, noise-filled datasets. Possessing these attributes uniquely qualifies the neural network as a mathematical basis for adaptability in personal biomedical devices. The purpose of this study was to determine the viability of neural networks in predicting Freezing of Gait (FoG), a symptom of Parkinson's disease in which the patient's legs are suddenly rendered unable to move. More specifically, a class of neural networks known as layered recurrent networks (LRNs) was applied to an open- source FoG experimental dataset donated to the Machine Learning Repository of the University of California at Irvine. The independent variables in this experiment \u2014 the subject being tested, neural network architecture, and sampling of the majority classes \u2014 were each varied and compared against the performance of the neural network in predicting future FoG events. It was determined that single-layered recurrent networks are a viable method of predicting FoG events given the volume of the training data available, though results varied significantly between different patients. For the three patients tested, shank acceleration data was used to train networks with peak precision/recall values of 41.88%/47.12%, 89.05%/29.60%, and 57.19%/27.39% respectively. These values were obtained for networks optimized using detection theory rather than optimized for desired values of precision and recall. Furthermore, due to the nature of the experiments performed in this study, these values are representative of the lower-bound performance of layered recurrent networks trained to detect gait freezing. As such, these values may be improved through a variety of measures.
ContributorsZia, Jonathan Sargon (Author) / Panchanathan, Sethuraman (Thesis director) / McDaniel, Troy (Committee member) / Adler, Charles (Committee member) / Electrical Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2016-05
Description
The effect of conflicting sensorimotor memories on optimal force strategies was explored. Subjects operated a virtual object controlled by a physical handle to complete a simple straight-line task. Perturbations applied to the handle induced a period of increased error in subject accuracy. After two blocks of 33 trials, perturbations switched direction, inducing increased error from the previous trials. Subjects returned after a 24-hour period to complete a similar protocol, but beginning with the second context and ending with the first. Interference from the first context on each day caused an increase in initial error for the second (P < 0.05). Following the rest period, subjects showed retention of the sensorimotor memory from the previous day through significantly decreased initial error (P = 3x10-6). However, subjects showed an increase in forces for each new context resulting from a sub-optimal motor strategy. Higher levels of total effort (P < 0.05) and a lack of separation between force values for opposing and non-opposing digits (P > 0.05) indicated a strategy that used more energy to complete the task, even when rates of learning appeared identical or improved. Two possible mechanisms for this lack of energy conservation have been proposed.
ContributorsSmith, Michael David (Author) / Santello, Marco (Thesis director) / Kleim, Jeffrey (Committee member) / Harrington Bioengineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2016-05
Description
In order to regain functional use of affected limbs, stroke patients must undergo intense, repetitive, and sustained exercises. For this reason, it is a common occurrence for the recovery of stroke patients to suffer as a result of mental fatigue and boredom. For this reason, serious games aimed at reproducing the movements patients practice during rehabilitation sessions, present a promising solution to mitigating patient psychological exhaustion. This paper presents a system developed at the Center for Cognitive Ubiquitous Computing (CubiC) at Arizona State University which provides a platform for the development of serious games for stroke rehabilitation. The system consists of a network of nodes called Smart Cubes based on the Raspberry Pi (model B) computer which have an array of sensors and actuators as well as communication modules that are used in-game. The Smart Cubes are modular, taking advantage of the Raspberry Pi's General Purpose Input/Output header, and can be augmented with additional sensors or actuators in response to the desires of game developers and stroke rehabilitation therapists. Smart Cubes present advantages over traditional exercises such as having the capacity to provide many different forms of feedback and allowing for dynamically adapting games. Smart Cubes also present advantages over modern serious gaming platforms in the form of their modularity, flexibility resulting from their wireless network topology, and their independence of a monitor. Our contribution is a prototype of a Smart Cube network, a programmable computing platform, and a software framework specifically designed for the creation of serious games for stroke rehabilitation.
ContributorsFakhri, Bijan (Author) / Panchanathan, Sethuraman (Thesis director) / McDaniel, Troy L. (Committee member) / Tadayon, Ramin (Committee member) / Barrett, The Honors College (Contributor) / Computer Science and Engineering Program (Contributor)
Created2014-05
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
This project aims to use the shape memory alloy nitinol as the basis for a biomimetic actuator. These actuators are designed to mimic the behavior of organic muscles for use in prosthetic and robotic devices. Actuator characterization included in the project examines the force output,electrical properties, and other variables relevant to actuator design.
ContributorsNoe, Cameron Scott (Author) / LaBelle, Jeffrey (Thesis director) / Santello, Marco (Committee member) / Barrett, The Honors College (Contributor) / Harrington Bioengineering Program (Contributor)
Created2014-05