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Description
This research is focused on two separate but related topics. The first uses an electroencephalographic (EEG) brain-computer interface (BCI) to explore the phenomenon of motor learning transfer. The second takes a closer look at the EEG-BCI itself and tests an alternate way of mapping EEG signals into machine commands. We test whether motor learning transfer is more related to use of shared neural structures between imagery and motor execution or to more generalized cognitive factors. Using an EEG-BCI, we train one group of participants to control the movements of a cursor using embodied motor imagery. A second group is trained to control the cursor using abstract motor imagery. A third control group practices moving the cursor using an arm and finger on a touch screen. We hypothesized that if motor learning transfer is related to the use of shared neural structures then the embodied motor imagery group would show more learning transfer than the abstract imaging group. If, on the other hand, motor learning transfer results from more general cognitive processes, then the abstract motor imagery group should also demonstrate motor learning transfer to the manual performance of the same task. Our findings support that motor learning transfer is due to the use of shared neural structures between imaging and motor execution of a task. The abstract group showed no motor learning transfer despite being better at EEG-BCI control than the embodied group. The fact that more participants were able to learn EEG-BCI control using abstract imagery suggests that abstract imagery may be more suitable for EEG-BCIs for some disabilities, while embodied imagery may be more suitable for others. In Part 2, EEG data collected in the above experiment was used to train an artificial neural network (ANN) to map EEG signals to machine commands. We found that our open-source ANN using spectrograms generated from SFFTs is fundamentally different and in some ways superior to Emotiv's proprietary method. Our use of novel combinations of existing technologies along with abstract and embodied imagery facilitates adaptive customization of EEG-BCI control to meet needs of individual users.
Contributorsda Silva, Flavio J. K (Author) / Mcbeath, Michael K (Thesis advisor) / Helms Tillery, Stephen (Committee member) / Presson, Clark (Committee member) / Sugar, Thomas (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
While pulse oximeter technology is not necessarily an area of new technology, advancements in performance and package of pulse sensors have been opening up the opportunities to use these sensors in locations other than the traditional finger monitoring location. This research report examines the full potential of creating a minimally invasive physiological and environmental observance method from the ear location. With the use of a pulse oximeter and accelerometer located within the ear, there is the opportunity to provide a more in-depth means to monitor a pilot for a Gravity-Induced Loss of Consciousness (GLOC) scenario while not adding any new restriction to the pilot's movement while in flight. Additionally, building from the GLOC scenario system, other safety monitoring systems for military and first responders are explored by alternating the physiological and environmental sensors. This work presents the design and development of hardware, signal processing algorithms, prototype development, and testing results of an in-ear wearable physiological sensor.
ContributorsNichols, Kevin (Author) / Redkar, Sangram (Thesis advisor) / Tripp Jr., Llyod (Committee member) / Dwivedi, Prabha (Committee member) / Sugar, Thomas (Committee member) / Arizona State University (Publisher)
Created2021
Description
Photoplethysmography (PPG) is a noninvasive optical signal that measures the change in blood volume. This particular signal can be interpreted to yield heart rate (HR) information which is commonly used in medical settings and diagnostics through wearable devices. The noninvasive nature of the measurement of the signal however causes it to be susceptible to noise sources such as motion artifacts (MA). This research starts by describing an end-to-end embedded HR estimation system that leverages noisy PPG and accelerometer data through machine learning (ML) to estimate HR. Through embedded ML for HR estimation, the limitations and challenges are highlighted, and a different HR estimation method is proposed. Next, a point-based value iteration (PBVI) framework is proposed to optimally select HR estimation filters based on the observed user activity. Lastly, the underlying dynamics of the PPG are explored in order to create a sparse dynamic expression of the PPG signal, which can be used to simulate PPG data to improve ML or remove MA from PPG.
ContributorsSindorf, Jacob (Author) / Redkar, Sangram (Thesis advisor) / Sugar, Thomas (Committee member) / Phatak, Amar (Committee member) / Arizona State University (Publisher)
Created2023
Description
While wearable soft robots have successfully addressed many inherent design limitations faced by wearable rigid robots, they possess a unique set of challenges due to their soft and compliant nature. Some of these challenges are present in the sensing, modeling, control and evaluation of wearable soft robots. Machine learning algorithms have shown promising results for sensor fusion with wearable robots, however, they require extensive data to train models for different users and experimental conditions. Modeling soft sensors and actuators require characterizing non-linearity and hysteresis, which complicates deriving an analytical model. Experimental characterization can capture the characteristics of non-linearity and hysteresis but requires developing a synthesized model for real-time control. Controllers for wearable soft robots must be robust to compensate for unknown disturbances that arise from the soft robot and its interaction with the user. Since developing dynamic models for soft robots is complex, inaccuracies that arise from the unmodeled dynamics lead to significant disturbances that the controller needs to compensate for. In addition, obtaining a physical model of the human-robot interaction is complex due to unknown human dynamics during walking. Finally, the performance of soft robots for wearable applications requires extensive experimental evaluation to analyze the benefits for the user. To address these challenges, this dissertation focuses on the sensing, modeling, control and evaluation of soft robots for wearable applications. A model-based sensor fusion algorithm is proposed to improve the estimation of human joint kinematics, with a soft flexible robot that requires compact and lightweight sensors. To overcome limitations with rigid sensors, an inflatable soft haptic sensor is developed to enable gait sensing and haptic feedback. Through experimental characterization, a mathematical model is derived to quantify the user's ground reaction forces and the delivered haptic force. Lastly, the performance of a wearable soft exosuit in assisting human users during lifting tasks is evaluated, and the benefits obtained from the soft robot assistance are analyzed.
ContributorsQuiñones Yumbla, Emiliano (Author) / Zhang, Wenlong (Thesis advisor) / Berman, Spring (Committee member) / Lee, Hyunglae (Committee member) / Marvi, Hamid (Committee member) / Sugar, Thomas (Committee member) / Arizona State University (Publisher)
Created2023
Description
This dissertation aimed to evaluate the effectiveness and drawbacks of promising fall prevention strategies in individuals with stroke by rigorously analyzing the biomechanics of laboratory falls and compensatory movements required to prevent a fall. Ankle-foot-orthoses (AFOs) and functional electrical stimulators (FESs) are commonly prescribed to treat foot drop. Despite well-established positive impacts of AFOs and FES devices on balance and gait, AFO and FES users fall at a high rate. In chapter 2 (as a preliminary study), solely mechanical impacts of a semi-rigid AFO on the compensatory stepping response of young healthy individuals following trip-like treadmill perturbations were evaluated. It was found that a semi-rigid AFO on the stepping leg diminished the propulsive impulse of the compensatory step which led to decreased trunk movement control, shorter step length, and reduced center of mass (COM) stability. These results highlight the critical role of plantarflexors in generating an effective compensatory stepping response. In chapter 3, the underlying biomechanical mechanisms leading to high fall risk in long-term AFO and FES users with chronic stroke were studied. It was found that AFO and FES users fall more than Non-users because they have a more impaired lower limb that is not fully addressed by AFO/FES, therefore leading to a more impaired compensatory stepping response characterized by increased inability to generate a compensatory step with paretic leg and decreased trunk movement control. An ideal future AFO that provides dorsiflexion assistance during the swing phase and plantarflexion assistance during the push-off phase of gait is suggested to enhance the compensatory stepping response and reduce more falls. In chapter 4, the effects of a single-session trip-specific training on the compensatory stepping response of individuals with stroke were evaluated. Trunk movement control was improved after a single session of training suggesting that this type of training is a viable option to enhance compensatory stepping response and reduce falls in individuals with stroke. Finally, a future powered AFO with plantarflexion assistance complemented by a trip-specific training program is suggested to enhance the compensatory stepping response and decrease falls in individuals with stroke.
ContributorsNevisipour, Masood (Author) / Honeycutt, Claire (Thesis advisor) / Sugar, Thomas (Thesis advisor) / Artemiadis, Panagiotis (Committee member) / Abbas, James (Committee member) / Lee, Hyunglae (Committee member) / Arizona State University (Publisher)
Created2019
Description
Traditionally, wearable exoskeletons for gait assistance have addressed the issue of high power requirement of providing support during walking. However, exoskeletons often are bulky, and suffer from misalignment of joints between the robot and the user. Soft robots in recent work have shown the ability to provide a high degree of compliance with a light weight and lower cost. This work presents the design, control, and evaluation of a soft inflatable exosuit to assist knee extension. First, the design of novel soft inflatable actuators of I cross-section and their application in the soft inflatable exosuit is presented. The actuators are applied to a soft and lightweight garment interface to assist in knee extension during the swing phase demonstrating reduced muscle activity for the quadriceps. Second, the control of the soft exosuit is presented with the introduction of a knee angle measurement system and smart shoe insole sensors. A new control method using human joint stiffness models as well as actuator models is developed. The new control method is evaluated with three users and a reduction in the sEMG activity of the quadriceps is observed with an increase in the activity of the hamstrings. Third, an improved version of the exosuit and a controller to assist knee extension in swing phase and initial stance are presented. The exosuit is applied to seven healthy and three impaired participants. Kinematics, muscle activity and gait compensations are studied. Reduced muscle activity for the quadriceps is seen in healthy participants with reduced execution times for functional activities such as timed up-and-go as well as sit-to-stand transitions in impaired participants. Finally, an untethered version of the soft exosuit using inflatable actuator composites and a portable pneumatic source are presented. Finite element models for the composites and inflatable actuators are generated and the actuators are characterized for performance. The design of a portable source for the exosuit is also presented. The inflatable actuator composites and the portable source are implemented in a portable exosuit system which demonstrated a reduction in the Vastus Lateralis activity during incline walking for three participants. Overall, this work investigated the feasibility of several versions of the soft exosuit for gait assistance.
ContributorsSridar, Saivimal (Author) / Zhang, Wenlong (Thesis advisor) / Sugar, Thomas (Committee member) / Lockhart, Thurmon (Committee member) / Arizona State University (Publisher)
Created2020
Description
The human ankle is a vital joint in the lower limb of the human body. As the point of interaction between the human neuromuscular system and the physical world, the ankle plays important role in lower extremity functions including postural balance and locomotion . Accurate characterization of ankle mechanics in lower extremity function is essential not just to advance the design and control of robots physically interacting with the human lower extremities but also in rehabilitation of humans suffering from neurodegenerative disorders.
In order to characterize the ankle mechanics and understand the underlying mechanisms that influence the neuromuscular properties of the ankle, a novel multi-axial robotic platform was developed. The robotic platform is capable of simulating various haptic environments and transiently perturbing the ankle to analyze the neuromechanics of the ankle, specifically the ankle impedance. Humans modulate ankle impedance to perform various tasks of the lower limb. The robotic platform is used to analyze the modulation of ankle impedance during postural balance and locomotion on various haptic environments. Further, various factors that influence modulation of ankle impedance were identified. Using the factors identified during environment dependent impedance modulation studies, the quantitative relationship between these factors, namely the muscle activation of major ankle muscles, the weight loading on ankle and the torque generation at the ankle was analyzed during postural balance and locomotion. A universal neuromuscular model of the ankle that quantitatively relates ankle stiffness, the major component of ankle impedance, to these factors was developed.
This neuromuscular model is then used as a basis to study the alterations caused in ankle behavior due to neurodegenerative disorders such as Multiple Sclerosis and Stroke. Pilot studies to validate the analysis of altered ankle behavior and demonstrate the effectiveness of robotic rehabilitation protocols in addressing the altered ankle behavior were performed. The pilot studies demonstrate that the altered ankle mechanics can be quantified in the affected populations and correlate with the observed adverse effects of the disability. Further, robotic rehabilitation protocols improve ankle control in affected populations as seen through functional improvements in postural balance and locomotion, validating the neuromuscular approach for rehabilitation.
In order to characterize the ankle mechanics and understand the underlying mechanisms that influence the neuromuscular properties of the ankle, a novel multi-axial robotic platform was developed. The robotic platform is capable of simulating various haptic environments and transiently perturbing the ankle to analyze the neuromechanics of the ankle, specifically the ankle impedance. Humans modulate ankle impedance to perform various tasks of the lower limb. The robotic platform is used to analyze the modulation of ankle impedance during postural balance and locomotion on various haptic environments. Further, various factors that influence modulation of ankle impedance were identified. Using the factors identified during environment dependent impedance modulation studies, the quantitative relationship between these factors, namely the muscle activation of major ankle muscles, the weight loading on ankle and the torque generation at the ankle was analyzed during postural balance and locomotion. A universal neuromuscular model of the ankle that quantitatively relates ankle stiffness, the major component of ankle impedance, to these factors was developed.
This neuromuscular model is then used as a basis to study the alterations caused in ankle behavior due to neurodegenerative disorders such as Multiple Sclerosis and Stroke. Pilot studies to validate the analysis of altered ankle behavior and demonstrate the effectiveness of robotic rehabilitation protocols in addressing the altered ankle behavior were performed. The pilot studies demonstrate that the altered ankle mechanics can be quantified in the affected populations and correlate with the observed adverse effects of the disability. Further, robotic rehabilitation protocols improve ankle control in affected populations as seen through functional improvements in postural balance and locomotion, validating the neuromuscular approach for rehabilitation.
ContributorsNalam, Varun (Author) / Lee, Hyunglae (Thesis advisor) / Artemiadis, Panagiotis (Committee member) / Santello, Marco (Committee member) / Sugar, Thomas (Committee member) / Lockhart, Thurmon (Committee member) / Arizona State University (Publisher)
Created2020
Description
Robotic assisted devices in gait rehabilitation have not seen penetration into clinical settings proportionate to the developments in this field. A possible reason for this is due to the development and evaluation of these devices from a predominantly engineering perspective. One way to mitigate this effect is to further include the principles of neurophysiology into the development of these systems. To further include these principles, this research proposes a method for grounded evaluation of three machine learning algorithms to gain insight on what modeling approaches are able to both replicate therapist assistance and emulate therapist strategies. The algorithms evaluated in this paper include ordinary least squares regression (OLS), gaussian process regression (GPR) and inverse reinforcement learning (IRL). The results show that grounded evaluation is able to provide evidence to support the algorithms at a higher resolution. Also, it was observed that GPR is likely the most accurate algorithm to replicate therapist assistance and to emulate therapist adaptation strategies.
ContributorsSmith, Mason Owen (Author) / Zhang, Wenlong (Thesis advisor) / Ben Amor, Hani (Committee member) / Sugar, Thomas (Committee member) / Arizona State University (Publisher)
Created2021
Description
This paper presents the design of a pneumatic actuator for a soft ankle-foot orthosis, called the Multi-material Actuator for Variable Stiffness (MAVS). This pneumatic actuator consists of an inflatable soft fabric actuator fixed between two layers of rigid retainer pieces. The MAVS is designed to be integrated with a soft robotic ankle-foot orthosis (SR-AFO) exosuit to aid in supporting the human ankle in the inversion/eversion directions. This design aims to assist individuals affected with chronic ankle instability (CAI) or other impairments to the ankle joint. The MAVS design is made from compliant fabric materials, layered and constrained by thin rigid retainers to prevent volume increase during actuation. The design was optimized to provide the greatest stiffness and least deflection for a beam positioned as a cantilever with a point load. The design of the MAVS took into account passive stiffness of the actuator when combining rigid and compliant materials so that stiffness is maximized when inflated and minimal when passive. An analytic model of the MAVS was created to evaluate the effects in stiffness observed by varying the ratio in length between the rigid pieces and the soft actuator. The results from the analytic model were compared to experimentally obtained results of the MAVS. The MAVS with the greatest stiffness was observed when the gap between the rigid retainers was smallest and the rigid retainer length was smallest. The MAVS design with the highest stiffness at 100 kPa was determined, which required 26.71 ± 0.06 N to deflect the actuator 20 mm, and a resulting stiffness of 1,335.5 N/m and 9.1% margin of error from the model predictions.
ContributorsHertzell, Tiffany (Author) / Lee, Hyunglae (Thesis director) / Sugar, Thomas (Committee member) / Mechanical and Aerospace Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2020-05