Thesis (Ph. D.)--Victoria University (Melbourne, Vic.), 2008. / Includes bibliographical references.

Thesis (M.S.)--University of Delaware, 2008. / Principal faculty advisor: David A. Barlow, Dept. of Health, Nutrition, & Exercise Sciences. Includes bibliographical references.

Thesis (M. Phil.)--University of Hong Kong, 1997. / Includes bibliographical references (leaf 254-261).

Thesis (M.S.)--Indiana University, 2002. / Includes bibliographical references (leaves 20-21).

Thesis (M. Sc.)--University of Oregon, 2006. / Includes bibliographical references.

Thesis (M.S.)--University of Oregon, 2006. / Includes bibliographical references (leaves 77-81). Also available online (PDF file) by a subscription to the set or by purchasing the individual file.

Multiple Sclerosis (MS) is a lifelong progressive neurologic disease of the central nervous system (CNS) that interrupts the flow of information within the brain and between the brain and the body, resulting in a variety of symptoms across the visual, sensory, motor, and autonomic functions. The concept of psychological resilience is emerging in clinical research, including research on MS, as a productive way to view the outcomes and experiences of living with a chronic disease and identify potential protective factors. The purpose of this dissertation was to examine the protective and predictive quality of psychological resilience in various domains of motor functioning. A sample of 130 patients underwent neuropsychological testing along with neurological examination at two distinct time points (baseline and 3-year follow-up). As part of each evaluation, patients were administered various tasks of motor functioning: the two-minute walk test (2MWT; a measure of gait endurance and stamina), timed 25-foot walk (T25FW; a measure of gait speed), nine-hole peg test (NHPT; a measure of upper extremity speed and coordination), grooved pegboard (G-Pegs; a measure of fine motor speed and dexterity), grip strength (Grip; a measure of upper body strength), and finger tapping test (FTT; a measure of simple motor speed), which served as this study’s outcomes. Psychological resilience, the primary predictor of interest, was operationalized as the self-reported ability of adapting well in the face of substantial adversity and significant sources of stress and was estimated using a validated self-report measure the Connor-Davidson Resilience Scale, 10 item version (CD-RISC-10). Additional predictors included mood, fatigue, demographic variables, disease variables, and magnetic resonance imaging (MRI) estimates. In contrast to our hypothesis, psychological resilience and functional outcomes were not correlated. Psychological resilience did not predict change in motor functioning over time and did not serve as a moderator between disease burden and motor functioning. As such, the present study does not provide support for psychological resilience as a protective factor for the motor system in MS or for resilience in predicting differential decline in motor functioning.

Gait analysis allows clinicians and researchers to quantitatively characterize the kinematics and kinetics of human movement. Devices that quantify gait can be either portable, such as instrumented shoes, or non-portable, such as motion capture systems and instrumented walkways. There is a tradeoff between these two classes of systems in terms of portability and accuracy. However, recent computer advances allow for the collection of meaningful data outside of the clinical setting. In this work, we present the DeepSole system combined with the different neural network models. This system is a fully capable to characterize the gait of the individuals and provide vibratory feedback to the wearer. Thanks to the flexible construction and its wireless capabilities, it can be comfortably worn by wide arrange of people, both able-bodied and people with pathologies that affect their gait. It can be used for characterization, training, and as an abstract sensor to measure human gait in real-time. Three neural network models were designed and implemented to map the sensors embedded in the DeepSole system to gait characteristics and events. The first one is a recurrent neural network that classifies the gait into the correct gait phase of the wearer. This model was validated with data from healthy young adults and children with Cerebral Palsy. Furthermore, this model was implemented in real-time to provide vibratory feedback to healthy young adults to create temporal asymmetry on the dominant side during regular walking. During the experiment, the subjects who walked had an increased stance time on both sides, but the dominant side was affected more. The second model is encoder-decoder recurrent neural network that maps the sensors into current gait cycle percentage. This model is useful to provide continuous feedback that is synchronized to the gait. This model was implemented in real-time to provide vibratory feedback to six muscle groups used during regular walking. The effects of the vibration were analyzed. It was found that depending on the feedback, the subjects changed their spatial and temporal gait parameters. The third model uses all the sensors in the instrumented footwear to identify a motor phenomenon called freezing of gait in patients with Parkinson’s Disease. This phenomenon is characterized by transient periods, usually lasting for several seconds, in which attempted ambulation is halted. The model has better performance than the state-of-the-art and does not require any pre-processing. The DeepSole system when used in conjunction with the presented models is able to characterize and provide feedback in a wide range of scenarios. The system is portable, comfortable, and can accommodate a wide range of populations who can benefit from this wearable technology.

本論文以表面肌電（Surface Electromypgraphy, SEMG）信號中動態信號能被獲取為前提，把被處理過的表面肌電信號轉變成步態參數 (gait parameters). 我們利用一些便攜式步態測量裝置，如加速度計，陀螺儀和腳踏開關和表面肌電圖測量裝置去採集步態參數。信號的處理和生物信息（身體的動態特性）轉換都加以討論和解釋，如濾波和預測肌肉的收縮等。 / 我們利用被採集步態參數作步態分析，並發現表面肌電信號內的動態信號的頻率特性能夠代表運動過程中的非恆久步態參數，如行走時的足部擺動的期間 (period of swing phase)、行走時的足部站立的期間 (period of stance phase) 和行走時的步幅期間 (period of stride)。 / 最後，我們發現可以利用線性預測 (linear prediction) 和閾值分析 (threshold analysis) 處理表面肌電信號以便獲得三種非恆久步態參數。根據我們的觀察，行走時足部擺動的期間可以被股直肌（rectus femoris, RF）的表面肌電信號捕獲，行走時的步幅期間可以被二頭肌股（bicep femoris, BF）的表面肌電信號捕獲，而行走時的足部站立的期間則可由BF和RF輸出的結果的平均值所捕獲。因此，表面肌電信號是可以作為一種獲取非恆久步態參數的工具。 / Electromyography (EMG) signal is an important quantity for describing the muscle’s activities and provides additional information in describing movement and locomotion in gait analysis. Surface electromyography (SEMG) measurement is a non-vivo technology for acquiring EMG signal. During the measurement of SEMG signals, the motion artifact is captured. Filters are applied to eliminate the frequency characteristics of motion artifact. However, this unwanted signal could be useful for obtaining the temporal gait parameters during the movement, such as the period of swing phase, the period of stance phase, and the period of stride of free walking. / In this study, accelerometers, gyroscopes and foot switches are used for the acquisition of kinematics and surface electromyography is used for measuring muscle’s activities. These measurement devices are evaluated in a gait study on lower extremity. The signal processing and conversion of bio-information (the dynamic characteristics of body) are discussed, such as filtering, and the prediction of muscle’s contraction. / Lastly, temporal gait parameters could be captured by SEMG measurement with the linear prediction process and threshold analysis. From the results, it is observed that the swing period can be captured through the SEMG measurement for rectus femoris (RF), the stride period can be captured by the SEMG measurement for bicep femoris (BF), and the stance period can be captured by the averaged result of the outputs of BF and RF. Thus, SEMG measurement could be a tool for capturing temporal gait parameters. / Detailed summary in vernacular field only. / Detailed summary in vernacular field only. / Detailed summary in vernacular field only. / Chan, Chi Chong. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2012. / Includes bibliographical references (leaves 67-69). / Abstracts also in Chinese. / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Literature Review --- p.1 / Chapter 1.2 --- Objectives --- p.5 / Chapter 1.3 --- Thesis Description --- p.5 / Chapter 2 --- Description for Wearable Gait Measurement --- p.7 / Chapter 2.1 --- Wearable Sensors --- p.8 / Chapter 2.2 --- Surface Electromyography (SEMG) --- p.12 / Chapter 2.3 --- Processing Unit --- p.15 / Chapter 2.4 --- Hardware Connection and Communication --- p.16 / Chapter 2.5 --- Summary --- p.20 / Chapter 3 --- Gait Analysis for Lower Extremity during Walking --- p.21 / Chapter 3.1 --- Gait Parameters Captured by Wearable Sensors --- p.21 / Chapter 3.1.1 --- Foot Switch: Walking Phase Detection --- p.22 / Chapter 3.1.2 --- Gyroscope: Frequency Response of Lower Limbs during Walking --- p.24 / Chapter 3.1.3 --- Accelerometer: Knee Joint Angle Estimation during Walking --- p.30 / Chapter 3.2 --- Analysis of Muscle Activities by SEMG signals --- p.36 / Chapter 3.3 --- Summary --- p.42 / Chapter 4 --- Temporal Gait Parameters during Walking by SEMG Measurement --- p.43 / Chapter 4.1 --- Motion Event and SEMG Signals --- p.43 / Chapter 4.2 --- Walking Phase Detection by SEMG Signals --- p.49 / Chapter 4.3 --- Temporal Gait Parameters --- p.53 / Chapter 4.4 --- Summary --- p.62 / Chapter 5 --- Conclusions, Contributions and Future Work --- p.63 / Chapter 5.1 --- Conclusions --- p.63 / Chapter 5.2 --- Contributions --- p.64 / Chapter 5.3 --- Future Work --- p.65 / Bibliography --- p.67

Falling during walking leads to millions of emergency room visits every year for all age groups and is a significant medical concern. While gait training has shown some promise for fall prevention, we know relatively little about how humans maintain stability, how we can quantify it and how we can use this knowledge to increase the success of fall prevention training. In this dissertation, I studied how human stability responds to continuous, small magnitude perturbations and to voluntary changes in gait characteristics by examining movement variability and long-term and instantaneous dynamic stability. In the first set of experiments, participants were exposed to continuous, pseudo-random external perturbations of the visual field and support surface in a Computer Assisted Rehabilitation ENvironment (CAREN). Participants exhibited increased step widths, shorter step lengths and increased step variability, orbital and short-term local instability. Despite this, mean instantaneous lateral stability remained approximately constant. In the second set of experiments, participants voluntarily adopted changes in their step widths and step lengths. Wider steps were associated with increased step width variability, decreased nonlinear stability, decreased anterior-posterior margins of stability and increased instantaneous lateral stability. Shorter steps were associated with decreased short-term and orbital stability but did not affect mean instantaneous stability. When instantaneous stability was examined between steps, as opposed to as an average over many steps, results from both studies indicated a relationship between each step’s stability and the stability of the immediately preceding step. From these studies, we now know that unpredictable, continuous perturbations during human walking applied in a given direction can be used to elicit predictable responses in motion variability and stability in that same direction. We know that the type of stability examined can influence the conclusions drawn about an individual’s stability during perturbed walking. For example, an individual’s variability may indicate increased risk of falling while he or she simultaneously demonstrates increased orbital stability and instantaneous lateral stability. A challenge faced in this area of research will be to understand how quantitative measures of stability relate to how we perceive our stability. / text