Matching Items (4)
Filtering by
- All Subjects: Motor Learning
- Genre: Doctoral Dissertation
- Creators: Santello, Marco
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
Brain-machine interfaces (BMIs) were first imagined as a technology that would allow subjects to have direct communication with prosthetics and external devices (e.g. control over a computer cursor or robotic arm movement). Operation of these devices was not automatic, and subjects needed calibration and training in order to master this control. In short, learning became a key component in controlling these systems. As a result, BMIs have become ideal tools to probe and explore brain activity, since they allow the isolation of neural inputs and systematic altering of the relationships between the neural signals and output. I have used BMIs to explore the process of brain adaptability in a motor-like task. To this end, I trained non-human primates to control a 3D cursor and adapt to two different perturbations: a visuomotor rotation, uniform across the neural ensemble, and a decorrelation task, which non-uniformly altered the relationship between the activity of particular neurons in an ensemble and movement output. I measured individual and population level changes in the neural ensemble as subjects honed their skills over the span of several days. I found some similarities in the adaptation process elicited by these two tasks. On one hand, individual neurons displayed tuning changes across the entire ensemble after task adaptation: most neurons displayed transient changes in their preferred directions, and most neuron pairs showed changes in their cross-correlations during the learning process. On the other hand, I also measured population level adaptation in the neural ensemble: the underlying neural manifolds that control these neural signals also had dynamic changes during adaptation. I have found that the neural circuits seem to apply an exploratory strategy when adapting to new tasks. Our results suggest that information and trajectories in the neural space increase after initially introducing the perturbations, and before the subject settles into workable solutions. These results provide new insights into both the underlying population level processes in motor learning, and the changes in neural coding which are necessary for subjects to learn to control neuroprosthetics. Understanding of these mechanisms can help us create better control algorithms, and design training paradigms that will take advantage of these processes.
ContributorsArmenta Salas, Michelle (Author) / Helms Tillery, Stephen I (Thesis advisor) / Si, Jennie (Committee member) / Buneo, Christopher (Committee member) / Santello, Marco (Committee member) / Kleim, Jeffrey (Committee member) / Arizona State University (Publisher)
Created2015
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
A current thrust in neurorehabilitation research involves exogenous neuromodulation of peripheral nerves to enhance neuroplasticity and maximize recovery of function. This dissertation presents the results of four experiments aimed at assessing the effects of trigeminal nerve stimulation (TNS) and occipital nerve stimulation (ONS) on motor learning, which was behaviorally characterized using an upper extremity visuomotor adaptation paradigm. In Aim 1a, the effects of offline TNS using clinically tested frequencies (120 and 60 Hz) were characterized. Sixty-three participants (22.75±4.6 y/o), performed a visuomotor rotation task and received TNS before encountering rotation of hand visual feedback. In Aim 1b, TNS at 3 kHz, which has been shown to be more tolerable at higher current intensities, was evaluated in 42 additional subjects (23.4±4.6 y/o). Results indicated that 3 kHz stimulation accelerated learning while 60 Hz stimulation slowed learning, suggesting a frequency-dependent effect on learning. In Aim 2, the effect of online TNS using 120 and 60 Hz were characterized to determine if this protocol would deliver better outcomes. Sixty-three participants (23.2±3.9 y/o) received either TNS or sham concurrently with perturbed visual feedback. Results showed no significant differences among groups. However, a cross-study comparison of results obtained with 60 Hz offline TNS showed a statistically significant improvement in learning rates with online stimulation relative to offline, suggesting a timing-dependent effect on learning. In Aim 3, TNS and ONS were compared using the best protocol from previous aims (offline 3 kHz). Additionally, concurrent stimulation of both nerves was explored to look for potential synergistic effects. Eighty-four participants (22.9±3.2 y/o) were assigned to one of four groups: TNS, ONS, TNS+ONS, and sham. Visual inspection of learning curves revealed that the ONS group demonstrated the fastest learning among groups. However, statistical analyses did not confirm this observation. In addition, the TNS+ONS group appeared to learn faster than the sham and TNS groups but slower than the ONS only group, suggesting no synergistic effects using this protocol, as initially hypothesized.
The results provide new information on the potential use of TNS and ONS in neurorehabilitation and performance enhancement in the motor domain.
ContributorsArias, Diego (Author) / Buneo, Christopher (Thesis advisor) / Schaefer, Sydney (Committee member) / Helms-Tillery, Stephen (Committee member) / Santello, Marco (Committee member) / Kleim, Jeffrey (Committee member) / Arizona State University (Publisher)
Created2023