Assessment of different gait patterns of daily living could provides useful information in studying one individual's stability and mobility during locomotion. As the foundation for better assessment of different gait patterns, the ability to automatically identity different patterns and walking surroundings provide valuable information for further understanding the relations between gait pattern and energy consumption. We apply Discrete Wavelet Transform (DWT) for feature generation and Fuzzy-logic based approach for designing the multi-class classifier to identify gait patterns among fiat walking, descending stairs, and ascending stairs based on continuous kinematic signals. / Falls in the aging population has always been one of the most challenging problems in public health care. We propose an automatic falling detection algorithm based on the analysis of plantar force on both feet, because plantar forces are an important parameters directly associated with postures of human locomotion. The proposed two-stage algorithm efficiently overcome the shortcomings of the widely proposed accelerometer or gyroscope based algorithms and could provide efficient assistant for automatic detection of falls once they occur. / Finally, the research of studying gait abnormalities is introduced. We develop the methodology for modeling and classifying abnormal gaits including toe-in, toe-out, over-supination, and heel walking via machine learning algorithms, hidden Markov models (HMM) and support vector machine (SVM) based on a suite of gait parameters. The trained classifiers can classify abnormal gait patterns mentioned above and the proposed methodology will make it possible to provide realtime feedback to assist persons with gait abnormalities in the development of a normal walking pattern in their daily life. / Keeping abnormal motion for long time will ultimately lead to pain in the feet, ankles, legs and skeletal disease, and badly influences the skelecton growth especially for children and adolescents. In biomedicine, gait analysis has been proved as an useful approach. in revealing helpful insights into the recognition of motion abnormalities. Analysis of gait is commonly used as a routine procedure in identifying movement or posture related abnormalities of humans and aiding the therapeutic processes. Our goal is to monitor and study gaits of humans in order that proper motion adjustments can he advised to improve their posture style and long-term well being. / Most currently utilized measurement systems for motion and gait analysis have the shortcomings of that the monitoring and analysis of motion is constrained in a limited environment and human-related assistance is essential. All of them cannot be acceptable for the purpose of long-term monitoring and studying of motion abnormalities. In this thesis, a new concept of an inexpensive, compact, and lightweight shoe-integrated platform is introduced. The shoe-integrated system is composed of a suite of sensors for wirelessly capturing gait parameters and generating well qualified analysis results. The ideal platform requires no specialized equipment or lab setup, allowing data to be collected not only in the narrow confines of a research lab, but essentially anywhere, both indoors and outdoors. / To be one of the common postural abnormalities, postural kyphosis is studied and modeled. We apply Cascade Neural Networks with Node-Decoupled Extended Kalman Filtering (CNN-NDEKF) to train the model for this binary classification problem. This proposed study is of particular significance to provide feedback in the application of postural kyphosis rectification. / Chen, Meng. / "December 2009." / Adviser: Yangsheng Xu. / Source: Dissertation Abstracts International, Volume: 72-01, Section: B, page: . / Thesis (Ph.D.)--Chinese University of Hong Kong, 2010. / Includes bibliographical references (leaves 120-130). / Electronic reproduction. Hong Kong : Chinese University of Hong Kong, [2012] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Electronic reproduction. Ann Arbor, MI : ProQuest Information and Learning Company, [200-] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Abstract also in Chinese.

The stopping of an earlier intended action is best explained in a race between a go process and a stop process (Logan & Cowan, 1984). The finish line, to which each process races, has been likened to a point of no return, specifically one that marks the onset of a final ballistic (unstoppable) process. Of note is the typical relation of reduced go probabilities and faster go latencies at shorter signal onset asynchronies (SOAs). (The SOA is the time interval between presentation of the go signal and presentation of the stop signal.) We report, in some cases, sub-maximal surface electromyograms (EMGs) at onset when trying to stop a maximal speeded action. These data indicate reduced synaptic drive to reach the motor pools as a result of earlier stopping effects and, as such, hold important implications for a theory of control. First, we interpret these data to suggest that the point of no return is phantom. Sub-maximal EMGs indicate a point in the control stream beyond which some EMG will be later observed but, importantly, they fail to mark the onset of a final ballistic process if, once breached, the same process remains subject to further effects of stopping. The alternative interpretation, however, that of a final ballistic process that receives sub-maximal input which results in sub-maximal output (i.e., EMG onset) cannot be ruled out from these data. We used the Hoffmann (H) reflex to probe further the mechanism of control for stopping a voluntary action. The H-reflex, an involuntary reflex that is taken as an index of spinal control, is relevant to the control of stopping because it is typically facilitated a short time before EMG onset. In other words, it provides a window of control within which a final ballistic process would otherwise be expected to locate. Thus, we interpret the effects of stopping on the H-reflex before EMG onset as strong evidence against a final ballistic process. Second, while the race model can explain the relation between the go probabilities, the go latencies and the SOAs, it fails to explain the sub-maximal EMG onsets that describe that same action in some cases. We submit a mechanism of excitatory-inhibitory interaction at all times up to the motor pool to explain both sets of empirical data. The viability of this theory is demonstrated using computer analyses.

The stopping of an earlier intended action is best explained in a race between a go process and a stop process (Logan & Cowan, 1984). The finish line, to which each process races, has been likened to a point of no return, specifically one that marks the onset of a final ballistic (unstoppable) process. Of note is the typical relation of reduced go probabilities and faster go latencies at shorter signal onset asynchronies (SOAs). (The SOA is the time interval between presentation of the go signal and presentation of the stop signal.) We report, in some cases, sub-maximal surface electromyograms (EMGs) at onset when trying to stop a maximal speeded action. These data indicate reduced synaptic drive to reach the motor pools as a result of earlier stopping effects and, as such, hold important implications for a theory of control. First, we interpret these data to suggest that the point of no return is phantom. Sub-maximal EMGs indicate a point in the control stream beyond which some EMG will be later observed but, importantly, they fail to mark the onset of a final ballistic process if, once breached, the same process remains subject to further effects of stopping. The alternative interpretation, however, that of a final ballistic process that receives sub-maximal input which results in sub-maximal output (i.e., EMG onset) cannot be ruled out from these data. We used the Hoffmann (H) reflex to probe further the mechanism of control for stopping a voluntary action. The H-reflex, an involuntary reflex that is taken as an index of spinal control, is relevant to the control of stopping because it is typically facilitated a short time before EMG onset. In other words, it provides a window of control within which a final ballistic process would otherwise be expected to locate. Thus, we interpret the effects of stopping on the H-reflex before EMG onset as strong evidence against a final ballistic process. Second, while the race model can explain the relation between the go probabilities, the go latencies and the SOAs, it fails to explain the sub-maximal EMG onsets that describe that same action in some cases. We submit a mechanism of excitatory-inhibitory interaction at all times up to the motor pool to explain both sets of empirical data. The viability of this theory is demonstrated using computer analyses. / Education, Faculty of / Kinesiology, School of / Graduate

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