ASU Electronic Theses and Dissertations
This collection includes most of the ASU Theses and Dissertations from 2011 to present. ASU Theses and Dissertations are available in downloadable PDF format; however, a small percentage of items are under embargo. Information about the dissertations/theses includes degree information, committee members, an abstract, supporting data or media.
In addition to the electronic theses found in the ASU Digital Repository, ASU Theses and Dissertations can be found in the ASU Library Catalog.
Dissertations and Theses granted by Arizona State University are archived and made available through a joint effort of the ASU Graduate College and the ASU Libraries. For more information or questions about this collection contact or visit the Digital Repository ETD Library Guide or contact the ASU Graduate College at gradformat@asu.edu.
This dissertation examined several hurdles that must be overcome to create a standardized method of calculating the LyE for gait data when collected with an accelerometer. In each of the following investigations, both the Rosenstein et al. and Wolf et al. algorithms as well as three normalization methods were applied in order to understand the extent at which these factors affect the LyE. First, the a priori parameters of time delay and embedding dimension which are required for phase space reconstruction were investigated. This study found that the time delay can be standardized to a value of 10 and that an embedding dimension of 5 or 7 should be used for the Rosenstein and Wolf algorithm respectively. Next, the effect of data length on the LyE was examined using 30 to 1300 strides of gait data. This analysis found that comparisons across papers are only possible when similar amounts of data are used but comparing across normalization methods is not recommended. And finally, the reliability and minimum required number of strides for each of the 6 algorithm-normalization method combinations in both young healthy and elderly adults was evaluated. This research found that the Rosenstein algorithm was more reliable and required fewer strides for the calculation of the LyE for an accelerometer.
This dissertation evaluates StartReact and the voluntary trials before and after exposure to StartReact during a point-to-point multi-jointed reach task to three different targets covering a large workspace. The results show that multi-jointed reach tasks are susceptible to StartReact in iwS and the distance, muscle and movement onset speed, and muscle activations percentages and amplitude increase during StartReact trials. In addition, the distance, accuracy, muscle and movement onsets speeds, and muscle synergy similarity indices to the norm synergies increase during the voluntary-initiated trials after exposure to StartReact. Overall, this dissertation shows that exposure to StartReact did not impair voluntary-initiated movement and muscle synergy, but even improved them. Therefore, this study suggests that StartReact is safe for more investigations in training studies and therapy.
In this thesis, a rehabilitative knee exoskeleton was designed which is significantly lighter, more portable and less costly to manufacture than existing designs. It accomplishes this performance by making use of high-powered and weight-optimized brushless DC (BLDC) electric motors designed for drones, open-source hardware and software solutions for robotic motion control, and rapid prototyping technologies such as 3D printing and laser cutting.
The exoskeleton is made from a series of laser cut aluminum plates spaced apart with off-the-shelf standoffs. A drone motor with a torque of 1.32 Nm powers an 18.5:1 reduction two-stage belt drive, giving a maximum torque of 24.4 Nm at the output. The bearings for the belt drive are installed into 3D printed bearing mounts, which act as a snug intermediary between the bearing and the aluminum plate. The system is powered off a 24 volt, 1,500 MAh lithium battery, which can provide power for around an hour of walking activity.
The exoskeleton is controlled with an ODrive motor controller connected to a Raspberry Pi. Hip angle data is provided by an IMU, and the knee angle is provided by an encoder on the output shaft. A compact Rotary Series Elastic Actuator (cRSEA) device is mounted on the output shaft as well, to accurately measure the output torque going to the wearer. A Proportional-Derivative (PD) controller with feedforward relates the input current with the output torque. The device was tested on a treadmill and found to have an average backdrive torque of 0.39 Nm, significantly lower than the current state of the art. A gravity compensation controller and impedance controller were implemented to assist during swing and stance phases respectively. The results were compared to the muscular exertion of the knee measured via Electromyography (EMG).
Chapter 3 discusses a magnetic needle tracking device with operative assistance from a six degree-of-freedom robotic arm. Traditional needle steering faces many obstacles such as torsional effects, buckling, and small radii of curvature. To improve upon the concept, this project uses permanent magnets in parallel with a tracking system to steer and determine the position and orientation of the needle in real time. The magnet configuration is located at the end effector of the robotic arm. The trajectory of the end effector depends on the needle’s path, and vice versa. The distance the needle travels inside the workspace is tracked by a direct current (DC) motor, to which the needle is tethered. Combining this length with the pose of the end effector, the position and orientation of the needle can be calculated. Simulation of this tracking device has shown the functionality of the system. Testing has been done to confirm that a single magnet pulls the needle through the phantom tissue.