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.
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- All Subjects: Biomechanics
Description
This thesis work presents two separate studies:The first study assesses standing balance under various 2-dimensional (2D) compliant environments simulated using a dual-axis robotic platform and vision conditions. Directional virtual time-to-contact (VTC) measures were introduced to better characterize postural balance from both temporal and spatial aspects, and enable prediction of fall-relevant directions. Twenty healthy young adults were recruited to perform quiet standing tasks on the platform. Conventional stability measures, namely center-of-pressure (COP) path length and COP area, were also adopted for further comparisons with the proposed VTC. The results indicated that postural balance was adversely impacted, evidenced by significant decreases in VTC and increases in COP path length/area measures, as the ground compliance increased and/or in the absence of vision (ps < 0.001). Interaction effects between environment and vision were observed in VTC and COP path length measures (ps ≤ 0.05), but not COP area (p = 0.103). The estimated likelihood of falls in anterior-posterior (AP) and medio-lateral (ML) directions converged to nearly 50% (almost independent of the foot setting) as the experimental condition became significantly challenging.
The second study introduces a deep learning approach using convolutional neural network (CNN) for predicting environments based on instant observations of sway during balance tasks. COP data were collected from fourteen subjects while standing on the 2D compliant environments. Different window sizes for data segmentation were examined to identify its minimal length for reliable prediction. Commonly-used machine learning models were also tested to compare their effectiveness with that of the presented CNN model. The CNN achieved above 94.5% in the overall prediction accuracy even with 2.5-second length data, which cannot be achieved by traditional machine learning models (ps < 0.05). Increasing data length beyond 2.5 seconds slightly improved the accuracy of CNN but substantially increased training time (60% longer). Importantly, averaged normalized confusion matrices revealed that CNN is much more capable of differentiating the mid-level environmental condition.
These two studies provide new perspectives in human postural balance, which cannot be
interpreted by conventional stability analyses. Outcomes of these studies contribute to the advancement of human interactive robots/devices for fall prevention and rehabilitation.
ContributorsPhan, Vu Nguyen (Author) / Lee, Hyunglae (Thesis advisor) / Peterson, Daniel (Committee member) / Marvi, Hamidreza (Committee member) / Arizona State University (Publisher)
Created2021
Description
The Atlantic razor clam burrows underground with effectiveness and efficiency by coordinating shape changings of its shell and foot. Inspired by the burrowing strategy of razor clams, this research is dedicated to developing a self-burrowing technology for active underground explorations by investigating the burrowing mechanism of razor clams from the perspective of soil mechanics. In this study, the razor clam was observed to burrow out of sands simply by extending and contracting its foot periodically. This upward burrowing gait is much simpler than its downward burrowing gait, which also involves opening/closing of the shell and dilation of the foot. The upward burrowing gait inspired the design of a self-burrowing-out soft robot, which drives itself out of sands naturally by extension and contraction through pneumatic inflation and deflation. A simplified analytical model was then proposed and explained the upward burrowing behavior of the robot and razor clams as the asymmetric nature of soil resistances applied on both ends due to the intrinsic stress gradient of sand deposits. To burrow downward, additional symmetry-breaking features are needed for the robot to increase the resistance in the upward burrowing direction and to decrease the resistance in the downward burrowing direction. A potential approach is by incorporating friction anisotropy, which was then experimentally demonstrated to affect the upward burrowing of the soft robot. The downward burrowing gait of razor clams provides another inspiration. By exploring the analogies between the downward burrowing gait and in-situ soil characterization methods, a clam-inspired shape-changing penetrator was designed and penetrated dry granular materials both numerically and experimentally. Results demonstrated that the shell opening not only contributes to forming a penetration anchor by compressing the surrounding particles, but also reduces the foot penetration resistance temporally by creating a stress arch above the foot; the shell closing facilitates the downward burrowing by reducing the friction resistance to the subsequent shell retraction. Findings from this research shed lights on the future design of a clam-inspired self-burrowing robot.
ContributorsHuang, Sichuan (Author) / Tao, Junliang (Thesis advisor) / Kavazanjian, Edward (Committee member) / Marvi, Hamidreza (Committee member) / Zapata, Claudia (Committee member) / van Paassen, Leon (Committee member) / Arizona State University (Publisher)
Created2020