Matching Items (34)
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- All Subjects: Biomedical Engineering
- Creators: Greger, Bradley
- Member of: Theses and Dissertations
- Member of: Barrett, The Honors College Thesis/Creative Project Collection
- Status: Published
Description
Humans' ability to perform fine object and tool manipulation is a defining feature of their sensorimotor repertoire. How the central nervous system builds and maintains internal representations of such skilled hand-object interactions has attracted significant attention over the past three decades. Nevertheless, two major gaps exist: a) how digit positions and forces are coordinated during natural manipulation tasks, and b) what mechanisms underlie the formation and retention of internal representations of dexterous manipulation. This dissertation addresses these two questions through five experiments that are based on novel grip devices and experimental protocols. It was found that high-level representation of manipulation tasks can be learned in an effector-independent fashion. Specifically, when challenged by trial-to-trial variability in finger positions or using digits that were not previously engaged in learning the task, subjects could adjust finger forces to compensate for this variability, thus leading to consistent task performance. The results from a follow-up experiment conducted in a virtual reality environment indicate that haptic feedback is sufficient to implement the above coordination between digit position and forces. However, it was also found that the generalizability of a learned manipulation is limited across tasks. Specifically, when subjects learned to manipulate the same object across different contexts that require different motor output, interference was found at the time of switching contexts. Data from additional studies provide evidence for parallel learning processes, which are characterized by different rates of decay and learning. These experiments have provided important insight into the neural mechanisms underlying learning and control of object manipulation. The present findings have potential biomedical applications including brain-machine interfaces, rehabilitation of hand function, and prosthetics.
ContributorsFu, Qiushi (Author) / Santello, Marco (Thesis advisor) / Helms Tillery, Stephen (Committee member) / Buneo, Christopher (Committee member) / Santos, Veronica (Committee member) / Artemiadis, Panagiotis (Committee member) / Arizona State University (Publisher)
Created2013
Description
Myoelectric control is lled with potential to signicantly change human-robot interaction.
Humans desire compliant robots to safely interact in dynamic environments
associated with daily activities. As surface electromyography non-invasively measures
limb motion intent and correlates with joint stiness during co-contractions,
it has been identied as a candidate for naturally controlling such robots. However,
state-of-the-art myoelectric interfaces have struggled to achieve both enhanced
functionality and long-term reliability. As demands in myoelectric interfaces trend
toward simultaneous and proportional control of compliant robots, robust processing
of multi-muscle coordinations, or synergies, plays a larger role in the success of the
control scheme. This dissertation presents a framework enhancing the utility of myoelectric
interfaces by exploiting motor skill learning and
exible muscle synergies for
reliable long-term simultaneous and proportional control of multifunctional compliant
robots. The interface is learned as a new motor skill specic to the controller,
providing long-term performance enhancements without requiring any retraining or
recalibration of the system. Moreover, the framework oers control of both motion
and stiness simultaneously for intuitive and compliant human-robot interaction. The
framework is validated through a series of experiments characterizing motor learning
properties and demonstrating control capabilities not seen previously in the literature.
The results validate the approach as a viable option to remove the trade-o
between functionality and reliability that have hindered state-of-the-art myoelectric
interfaces. Thus, this research contributes to the expansion and enhancement of myoelectric
controlled applications beyond commonly perceived anthropomorphic and
\intuitive control" constraints and into more advanced robotic systems designed for
everyday tasks.
Humans desire compliant robots to safely interact in dynamic environments
associated with daily activities. As surface electromyography non-invasively measures
limb motion intent and correlates with joint stiness during co-contractions,
it has been identied as a candidate for naturally controlling such robots. However,
state-of-the-art myoelectric interfaces have struggled to achieve both enhanced
functionality and long-term reliability. As demands in myoelectric interfaces trend
toward simultaneous and proportional control of compliant robots, robust processing
of multi-muscle coordinations, or synergies, plays a larger role in the success of the
control scheme. This dissertation presents a framework enhancing the utility of myoelectric
interfaces by exploiting motor skill learning and
exible muscle synergies for
reliable long-term simultaneous and proportional control of multifunctional compliant
robots. The interface is learned as a new motor skill specic to the controller,
providing long-term performance enhancements without requiring any retraining or
recalibration of the system. Moreover, the framework oers control of both motion
and stiness simultaneously for intuitive and compliant human-robot interaction. The
framework is validated through a series of experiments characterizing motor learning
properties and demonstrating control capabilities not seen previously in the literature.
The results validate the approach as a viable option to remove the trade-o
between functionality and reliability that have hindered state-of-the-art myoelectric
interfaces. Thus, this research contributes to the expansion and enhancement of myoelectric
controlled applications beyond commonly perceived anthropomorphic and
\intuitive control" constraints and into more advanced robotic systems designed for
everyday tasks.
ContributorsIson, Mark (Author) / Artemiadis, Panagiotis (Thesis advisor) / Santello, Marco (Committee member) / Greger, Bradley (Committee member) / Berman, Spring (Committee member) / Sugar, Thomas (Committee member) / Fainekos, Georgios (Committee member) / Arizona State University (Publisher)
Created2015
Description
Optical Fibers coupled to laser light sources, and Light Emitting Diodes are the two classes of technologies used for optogenetic experiments. Arizona State University's Flexible Display Center fabricates novel flexible Organic Light Emitting Diodes(OLEDs). These OLEDs have the capability of being monolithically fabricated over flexible, transparent plastic substrates and having power efficient ways of addressing high density arrays of LEDs. This thesis critically evaluates the technology by identifying the key advantages, current limitations and experimentally assessing the technology in in-vivo and in-vitro animal models. For in-vivo testing, the emitted light from a flat OLED panel was directly used to stimulate the neo-cortex in the M1 region of transgenic mice expressing ChR2 (B6.Cg-Tg (Thy1-ChR2/EYFP) 9Gfng/J). An alternative stimulation paradigm using a collimating optical system coupled with an optical fiber was used for stimulating neurons in layer 5 of the motor cortex in the same transgenic mice. EMG activity was recorded from the contralateral vastus lateralis muscles. In vitro testing of the OLEDs was done in primary cortical neurons in culture transfected with blue light sensitive ChR2. The neurons were cultured on a microelectrode array for taking neuronal recordings.
ContributorsShah, Ankur (Author) / Muthuswamy, Jitendran (Thesis advisor) / Greger, Bradley (Committee member) / Blain Christen, Jennifer (Committee member) / Arizona State University (Publisher)
Created2015
Description
Epilepsy is a group of disorders that cause seizures in approximately 2.2 million people in the United States. Over 30% of these patients have epilepsies that do not respond to treatment with anti-epileptic drugs. For this population, focal resection surgery could offer long-term seizure freedom. Surgery candidates undergo a myriad of tests and monitoring to determine where and when seizures occur. The “gold standard” method for focus identification involves the placement of electrocorticography (ECoG) grids in the sub-dural space, followed by continual monitoring and visual inspection of the patient’s cortical activity. This process, however, is highly subjective and uses dated technology. Multiple studies were performed to investigate how the evaluation process could benefit from an algorithmic adjust using current ECoG technology, and how the use of new microECoG technology could further improve the process.
Computational algorithms can quickly and objectively find signal characteristics that may not be detectable with visual inspection, but many assume the data are stationary and/or linear, which biological data are not. An empirical mode decomposition (EMD) based algorithm was developed to detect potential seizures and tested on data collected from eight patients undergoing monitoring for focal resection surgery. EMD does not require linearity or stationarity and is data driven. The results suggest that a biological data driven algorithm could serve as a useful tool to objectively identify changes in cortical activity associated with seizures.
Next, the use of microECoG technology was investigated. Though both ECoG and microECoG grids are composed of electrodes resting on the surface of the cortex, changing the diameter of the electrodes creates non-trivial changes in the physics of the electrode-tissue interface that need to be accounted for. Experimenting with different recording configurations showed that proper grounding, referencing, and amplification are critical to obtain high quality neural signals from microECoG grids.
Finally, the relationship between data collected from the cortical surface with micro and macro electrodes was studied. Simultaneous recordings of the two electrode types showed differences in power spectra that suggest the inclusion of activity, possibly from deep structures, by macroelectrodes that is not accessible by microelectrodes.
Computational algorithms can quickly and objectively find signal characteristics that may not be detectable with visual inspection, but many assume the data are stationary and/or linear, which biological data are not. An empirical mode decomposition (EMD) based algorithm was developed to detect potential seizures and tested on data collected from eight patients undergoing monitoring for focal resection surgery. EMD does not require linearity or stationarity and is data driven. The results suggest that a biological data driven algorithm could serve as a useful tool to objectively identify changes in cortical activity associated with seizures.
Next, the use of microECoG technology was investigated. Though both ECoG and microECoG grids are composed of electrodes resting on the surface of the cortex, changing the diameter of the electrodes creates non-trivial changes in the physics of the electrode-tissue interface that need to be accounted for. Experimenting with different recording configurations showed that proper grounding, referencing, and amplification are critical to obtain high quality neural signals from microECoG grids.
Finally, the relationship between data collected from the cortical surface with micro and macro electrodes was studied. Simultaneous recordings of the two electrode types showed differences in power spectra that suggest the inclusion of activity, possibly from deep structures, by macroelectrodes that is not accessible by microelectrodes.
ContributorsAshmont, Kari Rich (Author) / Greger, Bradley (Thesis advisor) / Helms Tillery, Stephen (Committee member) / Buneo, Christopher (Committee member) / Adelson, P David (Committee member) / Dudek, F Edward (Committee member) / Arizona State University (Publisher)
Created2015
Description
Robust and stable decoding of neural signals is imperative for implementing a useful neuroprosthesis capable of carrying out dexterous tasks. A nonhuman primate (NHP) was trained to perform combined flexions of the thumb, index and middle fingers in addition to individual flexions and extensions of the same digits. An array of microelectrodes was implanted in the hand area of the motor cortex of the NHP and used to record action potentials during finger movements. A Support Vector Machine (SVM) was used to classify which finger movement the NHP was making based upon action potential firing rates. The effect of four feature selection techniques, Wilcoxon signed-rank test, Relative Importance, Principal Component Analysis, and Mutual Information Maximization was compared based on SVM classification performance. SVM classification was used to examine the functional parameters of (i) efficacy (ii) endurance to simulated failure and (iii) longevity of classification. The effect of using isolated-neuron and multi-unit firing rates was compared as the feature vector supplied to the SVM. The best classification performance was on post-implantation day 36, when using multi-unit firing rates the worst classification accuracy resulted from features selected with Wilcoxon signed-rank test (51.12 ± 0.65%) and the best classification accuracy resulted from Mutual Information Maximization (93.74 ± 0.32%). On this day when using single-unit firing rates, the classification accuracy from the Wilcoxon signed-rank test was 88.85 ± 0.61 % and Mutual Information Maximization was 95.60 ± 0.52% (degrees of freedom =10, level of chance =10%)
ContributorsPadmanaban, Subash (Author) / Greger, Bradley (Thesis advisor) / Santello, Marco (Thesis advisor) / Helms Tillery, Stephen (Committee member) / Arizona State University (Publisher)
Created2015
Description
Lower-limb prosthesis users have commonly-recognized deficits in gait and posture control. However, existing methods in balance and mobility analysis fail to provide sufficient sensitivity to detect changes in prosthesis users' postural control and mobility in response to clinical intervention or experimental manipulations and often fail to detect differences between prosthesis users and non-amputee control subjects. This lack of sensitivity limits the ability of clinicians to make informed clinical decisions and presents challenges with insurance reimbursement for comprehensive clinical care and advanced prosthetic devices. These issues have directly impacted clinical care by restricting device options, increasing financial burden on clinics, and limiting support for research and development. This work aims to establish experimental methods and outcome measures that are more sensitive than traditional methods to balance and mobility changes in prosthesis users. Methods and analysis techniques were developed to probe aspects of balance and mobility control that may be specifically impacted by use of a prosthesis and present challenges similar to those experienced in daily life that could improve the detection of balance and mobility changes. Using the framework of cognitive resource allocation and dual-tasking, this work identified unique characteristics of prosthesis users’ postural control and developed sensitive measures of gait variability. The results also provide broader insight into dual-task analysis and the motor-cognitive response to demanding conditions. Specifically, this work identified altered motor behavior in prosthesis users and high cognitive demand of using a prosthesis. The residual standard deviation method was developed and demonstrated to be more effective than traditional gait variability measures at detecting the impact of dual-tasking. Additionally, spectral analysis of the center of pressure while standing identified altered somatosensory control in prosthesis users. These findings provide a new understanding of prosthetic use and new, highly sensitive techniques to assess balance and mobility in prosthesis users.
ContributorsHoward, Charla Lindley (Author) / Abbas, James (Thesis advisor) / Buneo, Christopher (Committee member) / Lynskey, Jim (Committee member) / Santello, Marco (Committee member) / Artemiadis, Panagiotis (Committee member) / Arizona State University (Publisher)
Created2017
Description
Object manipulation is a common sensorimotor task that humans perform to interact with the physical world. The first aim of this dissertation was to characterize and identify the role of feedback and feedforward mechanisms for force control in object manipulation by introducing a new feature based on force trajectories to quantify the interaction between feedback- and feedforward control. This feature was applied on two grasp contexts: grasping the object at either (1) predetermined or (2) self-selected grasp locations (“constrained” and “unconstrained”, respectively), where unconstrained grasping is thought to involve feedback-driven force corrections to a greater extent than constrained grasping. This proposition was confirmed by force feature analysis. The second aim of this dissertation was to quantify whether force control mechanisms differ between dominant and non-dominant hands. The force feature analysis demonstrated that manipulation by the dominant hand relies on feedforward control more than the non-dominant hand. The third aim was to quantify coordination mechanisms underlying physical interaction by dyads in object manipulation. The results revealed that only individuals with worse solo performance benefit from interpersonal coordination through physical couplings, whereas the better individuals do not. This work showed that naturally emerging leader-follower roles, whereby the leader in dyadic manipulation exhibits significant greater force changes than the follower. Furthermore, brain activity measured through electroencephalography (EEG) could discriminate leader and follower roles as indicated power modulation in the alpha frequency band over centro-parietal areas. Lastly, this dissertation suggested that the relation between force and motion (arm impedance) could be an important means for communicating intended movement direction between biological agents.
ContributorsMojtahedi, Keivan (Author) / Santello, Marco (Thesis advisor) / Greger, Bradley (Committee member) / Artemiadis, Panagiotis (Committee member) / Helms Tillery, Stephen (Committee member) / Buneo, Christopher (Committee member) / Arizona State University (Publisher)
Created2017
Description
Progressive gait disorder in Parkinson's disease (PD) is usually exhibited as reduced step/stride length and gait speed. People with PD also exhibit stooped posture, which can contribute to reduced step length and arm swing. Since gait and posture deficits in people with PD do not respond well to pharmaceutical and surgical treatments, novel rehabilitative therapies to alleviate these impairments are necessary. Many studies have confirmed that people with PD can improve their walking patterns when external cues are presented. Only a few studies have provided explicit real-time feedback on performance, but they did not report how well people with PD can follow the cues on a step-by-step basis. In a single-session study using a novel-treadmill based paradigm, our group had previously demonstrated that people with PD could follow step-length and back angle feedback and improve their gait and posture during treadmill walking. This study investigated whether a long-term (6-week, 3 sessions/week) real-time feedback training (RTFT) program can improve overground gait, upright posture, balance, and quality of life. Three subjects (mean age 70 ± 2 years) with mild to moderate PD (Hoehn and Yahr stage III or below) were enrolled and participated in the program. The RTFT sessions involved walking on a treadmill while following visual feedback of step length and posture (one at any given time) displayed on a monitor placed in front of the subject at eye-level. The target step length was set between 110-120% of the step length obtained during a baseline non-feedback walking trial and the target back angle was set at the maximum upright posture exhibited during a quiet standing task. Two subjects were found to significantly improve their posture and overground walking at post-training and these changes were retained six weeks after RTFT (follow-up) and the third subject improved his upright posture and gait rhythmicity. Furthermore, the magnitude of the improvements observed in these subjects was greater than the improvements observed in reports on other neuromotor interventions. These results provide preliminary evidence that real-time feedback training can be used as an effective rehabilitative strategy to improve gait and upright posture in people with PD.
ContributorsBaskaran, Deepika (Author) / Krishnamurthi, Narayanan (Thesis advisor) / Abbas, James (Thesis advisor) / Honeycutt, Claire (Committee member) / Arizona State University (Publisher)
Created2017
Description
This work presents the integration of user intent detection and control in the development of the fluid-driven, wearable, and continuum, Soft Poly-Limb (SPL). The SPL utilizes the numerous traits of soft robotics to enable a novel approach to provide safe and compliant mobile manipulation assistance to healthy and impaired users. This wearable system equips the user with an additional limb made of soft materials that can be controlled to produce complex three-dimensional motion in space, like its biological counterparts with hydrostatic muscles. Similar to the elephant trunk, the SPL is able to manipulate objects using various end effectors, such as suction adhesion or a soft grasper, and can also wrap its entire length around objects for manipulation. User control of the limb is demonstrated using multiple user intent detection modalities. Further, the performance of the SPL studied by testing its capability to interact safely and closely around a user through a spatial mobility test. Finally, the limb’s ability to assist the user is explored through multitasking scenarios and pick and place tests with varying mounting locations of the arm around the user’s body. The results of these assessments demonstrate the SPL’s ability to safely interact with the user while exhibiting promising performance in assisting the user with a wide variety of tasks, in both work and general living scenarios.
ContributorsVale, Nicholas Marshall (Author) / Polygerinos, Panagiotis (Thesis advisor) / Zhang, Wenlong (Committee member) / Artemiadis, Panagiotis (Committee member) / Arizona State University (Publisher)
Created2018
Description
Locomotion is of prime importance in enabling human beings to effectively respond
in space and time to meet different needs. Approximately 2 million Americans live
with an amputation with most of those amputations being of the lower limbs. To
advance current state-of-the-art lower limb prosthetic devices, it is necessary to adapt
performance at a level of intelligence seen in human walking. As such, this thesis
focuses on the mechanisms involved during human walking, while transitioning from
rigid to compliant surfaces such as from pavement to sand, grass or granular media.
Utilizing a unique tool, the Variable Stiffness Treadmill (VST), as the platform for
human walking, rigid to compliant surface transitions are simulated. The analysis of
muscular activation during the transition from rigid to different compliant surfaces
reveals specific anticipatory muscle activation that precedes stepping on a compliant
surface. There is also an indication of varying responses for different surface stiffness
levels. This response is observed across subjects. Results obtained are novel and
useful in establishing a framework for implementing control algorithm parameters to
improve powered ankle prosthesis. With this, it is possible for the prosthesis to adapt
to a new surface and therefore resulting in a more robust smart powered lower limb
prosthesis.
in space and time to meet different needs. Approximately 2 million Americans live
with an amputation with most of those amputations being of the lower limbs. To
advance current state-of-the-art lower limb prosthetic devices, it is necessary to adapt
performance at a level of intelligence seen in human walking. As such, this thesis
focuses on the mechanisms involved during human walking, while transitioning from
rigid to compliant surfaces such as from pavement to sand, grass or granular media.
Utilizing a unique tool, the Variable Stiffness Treadmill (VST), as the platform for
human walking, rigid to compliant surface transitions are simulated. The analysis of
muscular activation during the transition from rigid to different compliant surfaces
reveals specific anticipatory muscle activation that precedes stepping on a compliant
surface. There is also an indication of varying responses for different surface stiffness
levels. This response is observed across subjects. Results obtained are novel and
useful in establishing a framework for implementing control algorithm parameters to
improve powered ankle prosthesis. With this, it is possible for the prosthesis to adapt
to a new surface and therefore resulting in a more robust smart powered lower limb
prosthesis.
ContributorsObeng, Ruby Afriyie (Author) / Artemiadis, Panagiotis (Thesis advisor) / Santello, Marco (Thesis advisor) / Lee, Hyunglae (Committee member) / Arizona State University (Publisher)
Created2019