Thesis (Ph. D.)--College of Computing, Georgia Institute of Technology, 2004. Directed by Aaron Bobick. / Vita. Includes bibliographical references (leaves 119-123).

Thesis (Ph.D.)--University of Delaware, 2009. / Principal faculty advisor: Jill S. Higginson, Dept. of Mechanical Engineering. Includes bibliographical references.

Thesis (Ph.D.)--University of Delaware, 2009. / Principal faculty advisor: Kurt T. Manal, Dept. of Mechanical Engineering. Includes bibliographical references.

Thesis (Ed. D.)--West Virginia University, 2003. / Title from document title page. Document formatted into pages; contains vi, 79 p. : ill. Vita. Includes abstract. Includes bibliographical references (p. 54-58).

The ability to switch between different forms of locomotion is critical to many aspects of survival, whether it is switching from walking to running to evade predators, or switching to a slower gait to obtain food. Uncovering the mechanisms behind gait transitions has implications for many fields, from treating Parkinson Disease to understanding the impact of drugs of abuse on movement. However, the mechanisms of gait transitions are not well understood. The experiments outlined in this thesis sought to understand the neuronal basis for gait switching. This work employed the nematode Caenorhabditis elegans, a unique model organism chosen for its genetic tractability and fully characterized nervous system. C. elegans displays different forms of motion: crawling on land and swimming in liquid. First, I sought to determine the mechanisms for switching between these forms of motion in collaboration with Dr. Andres Vidal-Gadea. In the process, we discovered that crawling and swimming actually represent distinct gaits in contrast to recent reports that suggested they were merely a single gait. We further elucidated mechanisms for gait transition in C. elegans. For instance, we found that the transition to crawling required viii the D1-like dopamine receptors DOP-1 and DOP-4; and activation of dopamine neurons via the light-activated cation channel Channelrhodopsin2 was sufficient to induce crawling behavior in worms immersed in liquid. Conversely, photoactivation of serotonergic neurons expressing Channelrhodopsin2 induced swim-like behavior on land. Finally, laser microablation of dopaminergic or serotonergic neurons was sufficient to impair the transition to crawl or swim, respectively. Together these results show that transitions to crawling and swimming are controlled by dopamine and serotonin respectively. Next I wanted to better understand how gait transitions are impaired by a drug of abuse, alcohol. I found that, as in other organisms, ethanol disrupts gait transitions, causing worms in water to inappropriately transition from swim to crawl and to display other land-specific behaviors. Animals lacking the D1-like dopamine receptor DOP-1 were resistant to the ethanol-induced transition to crawl. Finally, I found that several interneurons required for the transition to crawl. Specifically, laser microablation of the DOP-4 receptor-expressing neuron RID or the DOP-1-expressing neurons PQR or RIS resulted in a significant impairment in the time to crawl onset. Overall, the findings presented in this thesis represent the first evidence that C. elegans uses an evolutionarily conserved mechanism to transition between gaits and provides the beginning of a molecular description of gait transitions. / text

Trunk lean over the stance limb during gait has been linked to a reduction in the knee adduction moment, which is associated with joint loading. Differences were examined in knee adduction moments and frontal plane trunk lean during gait between subjects with knee osteoarthritis and a control group of healthy adults. Additionally, subject variability in human motion data presents a challenge to researchers when trying to detect differences between subject groups. The individual differences in neutral posture between subjects is a source of variation in joint angles. A method was developed using principal component analysis (PCA) to objectively reduce this inter subject variability. Gait analysis was performed on 80 subjects (40 osteoarthritis). Models were developed to define lateral thoracic tilt, as well as pelvic tilt. The trunk and pelvis frontal plane angles were used to describe trunk lean and pelvic tilt. Angles were calculated across the stance phase of gait. We analyzed the data, (i) by extracting discrete parameters (mean and peak) waveform values, and (ii) using principal component analysis (PCA) to extract shape and magnitude differences between the waveforms. Osteoarthritis (OA) subjects had a higher knee adduction moment than the control group (α=0.05). Although the discrete parameters for trunk lean did not show differences between groups, PCA did detect characteristic waveform differences between the control and osteoarthritis groups. The data show that subjects display similar waveform shapes, however waveforms vary in magnitude, suggesting a variation in posture between subjects. The results from the PCA reveal that the first PC, which captures the most variation in the data, represents this variation in magnitude. The second PC describes a significant difference in range of motion between the subject groups. Subjects with knee OA were found to have a different range of motion of their pelvis and trunk than control subjects. These changes are consistent with a strategy to lower the knee adduction moment. As an alternative to conventional subjective methods, PCA should be employed to reduce inter subject variability in order to ensure objective analysis in human motion waveform data. / Thesis (Master, Mechanical and Materials Engineering) -- Queen's University, 2009-08-14 14:43:53.127

Walking is the most common form of mobility in humans. For lower limb mobility impairments, a common treatment is to prescribe an ankle-foot orthosis (AFO) or brace, which is a passive device designed to resist undesired ankle-foot motion. Recent advances in actuator technology have led to the development of active AFOs (AAFOs). However, these devices are generally too bulky for everyday use and are limited to applications such as gait training for rehabilitation. The aim of this research was to investigate the feasibility of developing a novel Active Ankle-Foot Stabilizer (AAFS). The design criteria were mainly based on the strengths and limitations of existing AFOs. The sagittal plane functional requirements were determined using simulated gait data for elderly individuals and drop foot patients; however, it is intended that the device would be suitable for a wider range of disabilities including ankle sprains. A model of the foot was introduced to modify the moment of a deficient ankle where young healthy adult kinematics and kinetics were assumed. A moment deficit analysis was performed for different gait periods resulting in an AAFS model with two components: a linear rotational spring to modify the ankle joint rotational stiffness, and a torque source. The frontal plane functional requirements for the AAFS were modeled as a linear rotational spring which responded to particular gait events. A novel Variable Rotational Stiffness Actuator (VSRA) AFO was also investigated. It consisted of an actuated spring medial and lateral to the ankle to control sagittal plane ankle stiffness and a passive leafspring posterior to the ankle to control frontal plane ankle stiffness. Due to high forces and profile limitations, a spring and rotation actuator that satisfied the design criteria could not be developed, resulting in an infeasible design. Considering the high forces and moments required by the AAFS, a pneumatic approach was adopted. A novel Airbeam AFO, which consisted of a shank cuff and a foot plate to which airbeams were attached proximally and distally to the ankle, was examined. The joint rotational stiffness of the ankle would be controlled by the inflation of these individual cylindrical airbeams. To satisfy the functional requirements, the airbeam diameters and pressures were too large to meet the design criteria and were unrealistic for a portable device. Finally, a Pneumatic Sock AFO, which proved to best satisfy the functional requirements within the design criteria, was examined. The design consisted of an inner sock worn on the ankle, surrounded by anterior, posterior, medial, and lateral bladders which inflate against outer fabric shells. Although promising, the Pneumatic Sock AFO requires further investigation in regards to manufacturing and behaviour characterization before a functional prototype can be developed. Mechanical test methods to characterize the behaviour of the Pneumatic Sock AFO in the sagittal and frontal planes were developed including the control components required, the configuration of a test rig, and test procedures.

This thesis aims to apply neural networks in the classification of human patterns of movement and to compare the accuracy of this technique with existing methods (conventional statistics and clinical assessment). Three different examples of human movement and one of posture were chosen for study and a variety of biomechanical parameters used to describe them. The temporal parameters of gait patterns, related to speed of walking and walking with splinted knee or weighted leg, were recorded. The angular displacement of both hips and knees was measured during stepping up or down steps of five different heights. Different standing postures were studied by measuring the disposition of body landmarks associated with imagined moods of human subjects. Finally, changes of the sit-stand-sit manoeuvre due to chronic low back pain, expressed as joint movement and forces exerted on the ground, were recorded. Patterns were classified by neural networks, linear discriminant analysis and, in the case of sit-stand patterns, by qualified clinicians. By altering the number of variables to discriminate between patterns, benefits of the above classifiers were identified. The success in classification of the measured patterns by neural networks was found to have an accuracy at least as high as that of linear discriminant analysis. A neural network is a useful tool for the discrimination of patterns of human movements; its main advantage is the ability to deal with a large number of predictor variables. A successfully trained and tested neural networks can easily be set up in a computer and, on the evidence presented, could be used to help clinicians diagnose or assess pathological patterns of movement.

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