Master of Science (M.S.), University of Dayton, 2020, Mechanical Engineering

Ankle-foot orthoses (AFOs) are commonly prescribed to individuals with conditions such as stroke and multiple sclerosis (MS) to assist with foot drop and other gait deficits. While AFOs have obvious benefits, there is still a high fall rate among wearers. This is likely due to the rigidity and support provided by AFOs, restricting ankle movements, which could be helpful for recovery upon a slip or trip. While there are numerous studies that utilize a lean-and-release mechanism to examine reactive stepping in order to better understand fall mechanisms and fall recovery strategies, no study has used this mechanism to understand the impact of an AFO. The goal of this study was to examine, first in healthy young adults, differences in step recovery with and without an AFO using a lean-and-release paradigm. Twenty healthy, young adults completed a total of 30 reactive stepping trials (10 no AFO and 10 wearing the AFO on each leg) in a lean-and-release system. The forward lean angle was set to a 15º. To determine release, two retro-reflective markers were attached to the harness, which would separate upon release. Each participant had retro-reflective markers placed on anatomical locations of the back, hips, thighs, shank, and feet. Study participants were instructed to do whatever was necessary to regain their balance. All trials were recorded with a VICON motion capture system linked to two Bertec in-ground force plates, one for each foot. Temporal and kinematic variables were calculated, as well as stepping foot preference. Differences between conditions were determined by running a one-way ANOVA with a Tukey post-hoc in SPSS to compare. Participants, on average, stepped 1.4 times more frequently with the leg not wearing the AFO. Step length was significantly shorter (p<0.001) in the stepping leg AFO condition (0.56 ± 0.12 m), as compared to the no AFO condition (0.63 ± 0.09 m). However, step duration remained the same across all conditions, indicating changes in (open full item for complete abstract)

Committee: Kimberly Bigelow Ph.D. (Committee Chair); Kurt Jackson Ph.D., P.T. (Committee Member); Allison Kinney Ph.D. (Committee Member); David Myszka Ph.D.,P.E. (Committee Member) Subjects: Biomechanics; Mechanical Engineering

Master of Science, The Ohio State University, 2013, Electrical and Computer Engineering

When robots are integrated into the real world, chances are they will not be able to completely avoid situations in which they are bumped or pushed unexpectedly. In these situations, the robot could potentially damage itself, damage its surroundings, or fail to perform its tasking unless it is able to take active countermeasures to prevent or recover from falling. One such countermeasure, referred to as reactive stepping, involves a robot taking a series of steps in order to regain balance and recover from a push. Research into reactive stepping typically focuses on choosing which step to take. This thesis proposes a machine learning approach to reactive stepping. This approach leverages neural networks to calculate a series of steps that return the robot to a stable position. It was theorized that the robot would become stable if it always chose the step resulting in the highest reduction of energy. Theories were tested using a compass model that incorporated parameters and constraints realistic of an actual humanoid robot. The machine learning approach using neural networks performed favorably in both computation time and push recovery effectiveness when compared with the linear least squares, nearest interpolation, and linear interpolation methods. Results showed that when using neural networks to calculate the best step for an arbitrary push within the defined range, the compass model was able to successfully recover from 97% of the pushes applied. The procedure was kept very general and could be used to implement reactive stepping on physical robots, or other robot models.

Committee: Yuan Zheng (Advisor); David Orin (Committee Member) Subjects: Computer Science; Engineering; Robotics