Human Body Posture Classification by a Neural Fuzzy Network and Home Care System Application

Authors:
Chia-Feng Juang;Chia-Ming Chang
Affiliations:
Nat. Chung Hsing Univ., Taichung;-
Venue:
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Year:
2007

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Abstract

A new classification approach for human body postures based on a neural fuzzy network is proposed in this paper, and the approach is applied to detect emergencies that are caused by accidental falls. Four main body postures are used for posture classification, including standing, bending, sitting, and lying. After the human body is segmented from the background, the classification features are extracted from the silhouette. The body silhouette is projected onto horizontal and vertical axes, and then, a discrete Fourier transform is applied to each projected histogram. Magnitudes of significant Fourier transform coefficients together with the silhouette length-width ratio are used as features. The classifier is designed by a neural fuzzy network. The four postures can be classified with high accuracy according to experimental results. Classification results are also applicable to home care emergency detection of a person who suddenly falls and remains in the lying posture for a period of time due to experiments that were performed.