Analysis of Stroke Induced Motor Function Weakness in Post-Stroke Patients using Machine Learning

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The focus of this thesis is the development of a system that can analyze stroke induced motor weakness using pressure sensor mattresses. The proposed system utilizes time-series pressure data from publicly available datasets as well as data collected from recovering stroke patients at the Foothills Medical Center. Two tasks are performed with the pressure data. In the first task the incoming pressure data is classified into three body positions: supine, lateral, and prone on a frame-by-frame basis. The second task consists of classifying time-series pressure data into two classes: left-sided weakness and right-sided weakness. Results from the first task are used to improve results from the second task by only using patient data in which the patient is in a supine position. Extensive experiments are conducted using deep learning methodologies including convolutional neural networks and long-short term memory networks. The developed system is intended to be utilized to monitor patient condition throughout their stay at the hospital.
Bhatt, A. (2021). Analysis of stroke induced motor function weakness in post-stroke patients using machine learning (Master's thesis, University of Calgary, Calgary, Canada). Retrieved from