Theses and Dissertations
This collection includes both ASU Theses and Dissertations, submitted by graduate students, and the Barrett, Honors College theses submitted by undergraduate students.
Displaying 61 - 64 of 64
Description
Our ability to estimate the position of our body parts in space, a fundamentally proprioceptive process, is crucial for interacting with the environment and movement control. For proprioception to support these actions, the Central Nervous System has to rely on a stored internal representation of the body parts in space. However, relatively little is known about this internal representation of arm position. To this end, I developed a method to map proprioceptive estimates of hand location across a 2-d workspace. In this task, I moved each subject's hand to a target location while the subject's eyes were closed. After returning the hand, subjects opened their eyes to verbally report the location of where their fingertip had been. Then, I reconstructed and analyzed the spatial structure of the pattern of estimation errors. In the first couple of experiments I probed the structure and stability of the pattern of errors by manipulating the hand used and tactile feedback provided when the hand was at each target location. I found that the resulting pattern of errors was systematically stable across conditions for each subject, subject-specific, and not uniform across the workspace. These findings suggest that the observed structure of pattern of errors has been constructed through experience, which has resulted in a systematically stable internal representation of arm location. Moreover, this representation is continuously being calibrated across the workspace. In the next two experiments, I aimed to probe the calibration of this structure. To this end, I used two different perturbation paradigms: 1) a virtual reality visuomotor adaptation to induce a local perturbation, 2) and a standard prism adaptation paradigm to induce a global perturbation. I found that the magnitude of the errors significantly increased to a similar extent after each perturbation. This small effect indicates that proprioception is recalibrated to a similar extent regardless of how the perturbation is introduced, suggesting that sensory and motor changes may be two independent processes arising from the perturbation. Moreover, I propose that the internal representation of arm location might be constructed with a global solution and not capable of local changes.
ContributorsRincon Gonzalez, Liliana (Author) / Helms Tillery, Stephen I (Thesis advisor) / Buneo, Christopher A (Thesis advisor) / Santello, Marco (Committee member) / Santos, Veronica (Committee member) / Kleim, Jeffrey (Committee member) / Arizona State University (Publisher)
Created2012
Description
Although previous studies have elucidated the role of position feedback in the regulation of movement, the specific contribution of Golgi tendon organs (GTO) in force feedback, especially in stabilizing voluntary limb movements, has remained theoretical due to limitations in experimental techniques. This study aims to establish force feedback regulation mediated by GTO afferent signals in two phases. The first phase of this study consisted of simulations using a neuromusculoskeletal model of the monoarticular elbow flexor (MEF) muscle group, assess the impact of force feedback in maintaining steady state interaction forces against variable environmental stiffness. Three models were trained to accurately reach an interaction force of 40N, 50N and 60N respectively, using a fixed stiffness level. Next, the environment stiffness was switched between untrained levels for open loop (OL) and closed loop (CL) variants of the same model. Results showed that compared to OL, CL showed decreased force deviations by 10.43%, 12.11% and 13.02% for each of the models. Most importantly, it is also observed that in the absence of force feedback, environment stiffness is found to have an effect on the interaction force. In the second phase, human subjects were engaged in experiments utilizing an instrumented elbow exoskeleton that applied loads to the MEF muscle group, closely mimicking the simulation conditions. The experiments consisted of reference, blind and catch trial types, and 3 stiffness levels. Subjects were first trained to reach for a predetermined target force. During catch trials, stiffness levels were randomized between reaches. Responses obtained from these experiments showed that subjects were able to regulate forces with no significant effects of trial type or stiffness level. Since experimental results align closely with that of closed loop model simulations, the presence of force feedback mechanisms mediated by GTO within the human neuromuscular system is established. This study not only unveils the critical involvement of GTO in force feedback but also emphasizes the importance of understanding these mechanisms for developing advanced neuroprosthetics and rehabilitation strategies, shedding light on the intricate interplay between sensory inputs and motor responses in human proprioception.
ContributorsAbishek, Kevin (Author) / Lee, Hyunglae (Thesis advisor) / Buneo, Christopher (Committee member) / Santello, Marco (Committee member) / Arizona State University (Publisher)
Created2023
Description
This dissertation focuses on reinforcement learning (RL) controller design aiming for real-life applications in continuous state and control problems. It involves three major research investigations in the aspect of design, analysis, implementation, and evaluation. The application case addresses automatically configuring robotic prosthesis impedance parameters. Major contributions of the dissertation include the following. 1) An “echo control” using the intact knee profile as target is designed to overcome the limitation of a designer prescribed robotic knee profile. 2) Collaborative multiagent reinforcement learning (cMARL) is proposed to directly take into account human influence in the robot control design. 3) A phased actor in actor-critic (PAAC) reinforcement learning method is developed to reduce learning variance in RL. The design of an “echo control” is based on a new formulation of direct heuristic dynamic programming (dHDP) for tracking control of a robotic knee prosthesis to mimic the intact knee profile. A systematic simulation of the proposed control is provided using a human-robot system simulation in OpenSim. The tracking controller is then tested on able-bodied and amputee subjects. This is the first real-time human testing of RL tracking control of a robotic knee to mirror the profile of an intact knee. The cMARL is a new solution framework for the human-prosthesis collaboration (HPC) problem. This is the first attempt at considering human influence on human-robot walking with the presence of a reinforcement learning controlled lower limb prosthesis. Results show that treating the human and robot as coupled and collaborating agents and using an estimated human adaptation in robot control design help improve human walking performance. The above studies have demonstrated great potential of RL control in solving continuous problems. To solve more complex real-life tasks with multiple control inputs and high dimensional state space, high variance, low data efficiency, slow learning or even instability are major roadblocks to be addressed. A novel PAAC method is proposed to improve learning performance in policy gradient RL by accounting for both Q value and TD error in actor updates. Systematical and comprehensive demonstrations show its effectiveness by qualitative analysis and quantitative evaluation in DeepMind Control Suite.
ContributorsWu, Ruofan (Author) / Si, Jennie (Thesis advisor) / Huang, He (Committee member) / Santello, Marco (Committee member) / Papandreou- Suppappola, Antonia (Committee member) / Arizona State University (Publisher)
Created2023
Description
The current program of work explores the potential efficacy of textured insoles for improving biomechanical performance and cognitive acuity during static and dynamic performance. Despite the vast conceptual framework supporting the versatile benefits of textured insoles, the current literature has primarily focused on incorporating this treatment during low-phase movements within the diseased and elderly subset populations. The current study expands this research application by administering textured insole treatments to a healthy population during a physically demanding dynamic assessment and correlating the results to subjects' sensory perception. A convenience sample of 10 subjects was evaluated for their ability to maintain bilateral standing balance in a static condition and adapt to confined lane perturbations during standard track running. These evaluations were conducted under both control and textured insole conditions. Subjects also completed a visual analog scale test, rating the insole treatments based on surface roughness to establish a statistical relationship between individual perception and biomechanical performance. Results showed that textured insole treatments given intermediate ratings of perceived surface roughness significantly enhanced performance during bilateral standing balance and standard track running perturbation adaptation.
ContributorsBoll, Christopher Marly (Author) / Coza, Aurel (Thesis advisor) / Santello, Marco (Committee member) / Lockhart, Thurmon (Committee member) / Arizona State University (Publisher)
Created2024