Machine Learning for Gait Classification

Machine learning is a powerful tool for making predictions and has been widely used for solving various classification problems in last decades. As one of important applications of machine learning, gait classification focuses on distinguishing different gait patterns by investigating the quality of gait of individuals and categorizing them as belonging to particular classes. The most studied gait pattern classes are the normal gait patterns of healthy people, i.e., gait of people who do not have any gait disability caused by an illness or an injury, and the pathological gait of patients suffering from illnesses which cause gait disorders such as neurodegenerative diseases (NDDs). There has been significant research work trying to solve the gait classification problems using advanced machine learning techniques, as the results may be beneficial for the early detection of underlined NDDs and for the monitoring of the gait rehabilitation progress. Despite the huge development in the field of gait analysis and classification, there are still a number of challenges open to further research. One challenge is the optimization of applied machine learning strategies to achieve better classification results. Another challenge is to solve gait classification problems even in the case when only limited amount of data are available. Further, a challenge is the development of machine learning-based methods that could provide more precise results to evaluate the level of gait quality or gait disorder, in contrast of just classifying gait pattern as belonging to healthy or pathological gait. The focus of this thesis is on the development, implementation and evaluation of a novel and reliable solution for the complex gait classification problems by addressing the current challenges. This solution is presented as a classification framework that can be applied to different types of gait signals, such as lower-limbs joint angle signals, trunk acceleration signals, and stride interval signals. Developed framework incorporates a hybrid solution which combines two models to enhance the classification performance. In order to provide a large number of samples for training the models, a sample generation method is developed which could segments the gait signals into smaller fragments. Classification is firstly performed on the data sample level, and then the results are utilized to generate the subject-level results using a majority voting scheme. Besides the class labels, a confidence score is computed to interpret the level of gait quality. In order to significantly improve the gait classification performances, in this thesis a novel feature extraction methods are also proposed using statistical methods, as well as machine learning approaches. Gaussian mixture model (GMM), least square regression, and k-nearest neighbors (kNN) are employed to provide additional significant features. Promising classification results are achieved using the proposed framework and the extracted features. The framework is ultimately applied to the management of patients and their rehabilitation, and is proved to be feasible in many clinical scenarios, such as the evaluation of medication effect on Parkinsona s disease (PD) patientsa gait, the long-term gait monitoring of the hereditary spastic paraplegia (HSP) patient under physical therapy.