Behavior identification method based on fuzzy support vector machine

Abstract

The invention discloses a behavior identification method based on a fuzzy support vector machine and adopts the fuzzy support vector machine to realize identification for various human behaviors (including normal behaviors such as standing, walking, running, going upstairs / downstairs and abnormal behaviors such as falling down); the behavior identification method is mainly applied to eliminating the influences of isolated points and noise points in the sample points to classification effects and improving behavior identification precision. The main contents for realization of the behavior identification are as follows: firstly, behavior data acquisition is realized by using a tri-axial accelerometer to obtain an X-axis acceleration, a Y-axis acceleration and a Z-axis acceleration; the mean value, the variance and the energy of the resultant acceleration as well as the correlation coefficient between any two dimensions of three-dimensional data are respectively extracted by means of resultant acceleration extracting characteristic values, and a six-dimensional characteristic vector is obtained; secondly, the degree of membership of each sampling point to the affiliated classification is calculated; thirdly, the construction of a classification model is realized by using the fuzzy support vector machine; and fourthly, the identification for human behaviors is realized at the online stage.

Description

technical field [0001] The invention relates to the technical field of behavior recognition, in particular to a human behavior recognition method based on a fuzzy support vector machine. Background technique [0002] In recent years, health has become a hot spot that people pay attention to. The identification and monitoring of human behavior can indirectly estimate the amount of human activity. The identification of abnormal behavior (such as falls) can handle some unexpected situations very well, especially for the elderly. Word. Some living habits of the monitored person can also be obtained indirectly through the recognition of the behavior of the human body, and thus more physiological conditions of the human body can be obtained. [0003] Traditional human behavior recognition is based on image graphics. Although the development of image-based human behavior recognition technology is relatively early and the theory is relatively mature, there are deficiencies in image...

AI Technical Summary

Problems solved by technology

However, according to the theoretical analysis of support vector machines, it can be known that the support vector machine classification hyperplane is easily affected by noise points and isolated points, resulting in reduced classification accuracy.

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