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- Genre: Masters Thesis
Description
Multiple Sclerosis, an autoimmune disease, is one of the most common neurological disorder in which demyelinating of the axon occurs. The main symptoms of MS disease are fatigue, vision problems, stability issue, balance problems. Unfortunately, currently available treatments for this disease do not always guarantee the improvement of the condition of the MS patient and there has not been an accurate mechanism to measure the effectiveness of the treatment due to inter-patient heterogeneity. The factors that count for varying the performance of MS patients include environmental setting, weather, psychological status, dressing style and more. Also, patients may react differently while examined at specially arranged setting and this may not be the same while he/she is at home. Hence, it becomes a major problem for MS patients that how effectively a treatment slows down the progress of the disease and gives a relief for the patient. This thesis is trying to build a reliable system to estimate how good a treatment is for MS patients. Here I study the kinematic variables such as velocity of walking, stride length, variability and so on to find and compare the variations of the patient after a treatment given by the doctor, and trace these parameters for some patients after the treatment effect subdued.
ContributorsYin, Siyang (Author) / He, Jiping (Thesis advisor) / Pizziconi, Vincent (Committee member) / Towe, Bruce (Committee member) / Arizona State University (Publisher)
Created2012
Description
Virtual environments are used for many physical rehabilitation and therapy purposes with varying degrees of success. An important feature for a therapy environment is the real-time monitoring of a participants' movement performance. Such monitoring can be used to evaluate the environment in addition to the participant's learning. Methods for monitoring and evaluation include tracking kinematic performance as well as monitoring muscle and brain activities through EMG and EEG technology. This study aims to observe trends in individual participants' motor learning based on changes in kinematic parameters and use those parameters to characterize different types of learners. This information can then guide EEG/EMG data analysis in the future. The evaluation of motor learning using kinematic parameters of performance typically compares averages of pre- and post-data to identify patterns of changes of various parameters. A key issue with using pre- and post-data is that individual participants perform differently and have different time-courses of learning. Furthermore, different parameters can evolve at independent rates. Finally, there is great variability in the movements at early stages of learning a task. To address these issues, a combined approach is proposed using robust regression, piece-wise regression and correlation to categorize different participant's motor learning. Using the mixed reality rehabilitation system developed at Arizona State University, it was possible to engage participants in motor learning, as revealed by improvements in kinematic parameters. A combination of robust regression, piecewise regression and correlation were used to reveal trends and characterize participants based on motor learning of three kinematic parameters: trajectory error, supination error and the number of phases in the velocity profile.
ContributorsAttygalle, Suneth Satoshi (Author) / He, Jiping (Thesis advisor) / Rikakais, Thanassis (Committee member) / Iasemidis, Leonidas (Committee member) / Arizona State University (Publisher)
Created2010