We study the problem of analyzing and classifying frontal view human gait data by registration and modeling on a video data. In this study, we suppose that frontal view gait data as a mixing of scale changing, human movements and speed changing parameter. Our gait model is based on human gait structure and temporal-spatial relations between camera and subject. To demonstrate the effectiveness of our method, we conducted two sets of experiments, assessing the proposed method in gait analysis for young/elderly person and abnormal gait detecting. In abnormal gait detecting experiment, we apply K-NN classifier, using the estimated parameters, to perform normal/abnormal gait detect, and present results from an experiment involving 120 subjects (young person), and 60 subjects (elderly person). As a result, our method shows high detection rate.
Human Gait Modeling and Statistical Registration for the Frontal View Gait Data with Application to the Normal/Abnormal Gait Analysis
Lect. Notes Electrical Eng.
2013-09-12
15 pages
Aufsatz/Kapitel (Buch)
Elektronische Ressource
Englisch
Abnormal gait detection , Frontal view gait data , Gait analysis , Human gait modeling , K-nearest neighbor classifier , parameter estimation , Scale registration , speed estimation , statistical registration Engineering , Control, Robotics, Mechatronics , Software Engineering/Programming and Operating Systems , Mathematical and Computational Engineering , Electronic Circuits and Devices
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