title: Recognizing falls from silhouettes.
abstract: A major problem among the elderly involves falling. The recognition of falls from video first requires the segmentation of the individual from the background. To ensure privacy, segmentation should result in a silhouette that is a binary map indicating only the body position of the individual in an image. We have previously demonstrated a segmentation method based on color that can recognize the silhouette and detect and remove shadows. After the silhouettes are obtained, we extract features and train hidden Markov models to recognize future performances of these known activities. In this paper, we present preliminary results that demonstrate the usefulness of this approach for distinguishing between a few common activities, specifically with fall detection in mind.
authors.affiliation: Dept. of Electr. & Comput. Eng., Missouri Univ., Columbia, MO 65203, USA.
authors.forename: Derek
authors.initials: D
authors.lastname: Anderson
authors.forename: James M
authors.initials: JM
authors.lastname: Keller
authors.forename: Marjorie
authors.initials: M
authors.lastname: Skubic
authors.forename: Xi
authors.initials: X
authors.lastname: Chen
authors.forename: Zhihai
authors.initials: Z
authors.lastname: He
mesh_terms: D000058:Accidental Falls; D000061:Accidents, Home; D000368:Aged; D000465:Algorithms; D003201:Computers; D004867:Equipment Design; D006257:Head / Q000473:pathology; D006801:Humans; D007089:Image Enhancement; D007090:Image Interpretation, Computer-Assisted; D021621:Imaging, Three-Dimensional; D008390:Markov Chains; D016011:Normal Distribution; D010363:Pattern Recognition, Automated; D012984:Software; D014741:Video Recording