Intelligent shoes as platform to study human motion abnormality.

• Other Authors: Chen, Meng.
• Format: Others
• Language: English; Chinese
• Published: 2010
• Subjects: Biometric identification > Data processing; Gait in humans > Analysis; Human locomotion > Measurement
• Online Access: http://library.cuhk.edu.hk/record=b6074851; http://repository.lib.cuhk.edu.hk/en/item/cuhk-344484

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.