Computational Analysis of Upper Extremity Movements for People Post-Stroke

Wearable sensors have been beneficial in assessing motor impairment after stroke. Individuals who have experienced stroke may benefit from the use of wearable sensors to quantify and assess quality of motions in unobserved environments. Seven individuals participated in a study wherein they performed various gestures from the Fugl-Meyer Assessment (FMA), a measure of post-stroke impairment. Participants performed these gestures while being monitored by wearable sensors placed on each wrist. A series of MATLAB functions were written to process recorded sensor data, extract meaningful features from the data, and prepare those features for further use with various machine learning techniques. A combination of linear and nonlinear regression was applied to frequency domain values from each gesture to determine which can more accurately predict the time spent performing the gesture, and the associated gesture FMA score. General performance suggests that linear regression techniques appear to better fit paretic gestures, while nonlinear regression techniques appear to better fit non-paretic gestures. A use of classifier techniques were used to determine if a classifier can distinguish between paretic and non-paretic gestures. The combinations include determining if a higher performance is obtained through the use of either accelerometer, rate gyroscope, or both modalities combined. Our findings indicate that, for upper-extremity motion, classifiers trained using a combination of accelerometer and rate gyroscope data performed the best (accuracy of 73.1%). Classifiers trained using accelerometer data alone and rate gyroscope data alone performed slightly worse than the combined data classifier (70.2% and 65.7%, respectively). These results suggest specific features and methods suitable for the quantification of impairment after stroke

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