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        A study on machine learning algorithms for fall detection and movement classification

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        Date
        2009-12-15
        Author
        Ralhan, Amitoz Singh
        Type
        Thesis
        Degree Level
        Masters
        Abstract
        Fall among the elderly is an important health issue. Fall detection and movement tracking techniques are therefore instrumental in dealing with this issue. This thesis responds to the challenge of classifying different movement types as a part of a system designed to fulfill the need for a wearable device to collect data for fall and near-fall analysis. Four different fall activities (forward, backward, left and right), three normal activities (standing, walking and lying down) and near-fall situations are identified and detected. Different machine learning algorithms are compared and the best one is used for the real time classification. The comparison is made using Waikato Environment for Knowledge Analysis or in short WEKA. The system also has the ability to adapt to different gaits of different people. A feature selection algorithm is also introduced to reduce the number of features required for the classification problem.
        Degree
        Master of Science (M.Sc.)
        Department
        Electrical Engineering
        Program
        Electrical Engineering
        Supervisor
        Ko, Seok-Bum
        Committee
        Teng, Daniel; Ludwig, Simone; Dinh, Anh
        Copyright Date
        December 2009
        URI
        http://hdl.handle.net/10388/etd-12222009-144628
        Subject
        Machine Learning
        Fall Detection
        Feature Selection
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