Human behavior recognition method based on accelerometer

Abstract

The invention discloses a human behavior recognition and classification method based on an accelerometer. The method includes the first step of collecting human behavior samples as a training set, the second step of searching for a projection matrix U which is optimal for the recognition and classification of the training set, the third step of carrying projection on no-labeled data, and the fourth step of classifying the projected data by using a minimum distance classifier to obtain a recognition result. According to the human behavior recognition method, a partial approximate linear hypothesis is carried out on adjacent blocks formed by labeled data so as to enable the distance between different types of samples on the blocks to be large enough, positional sequence information of the same type of samples is reserved as far as possible through class sigmoid function penalty factors, and finally a global objective function is established on the basis of the objective functions on all blocks. The human behavior recognition method can reserve the information of the distance between the samples in a higher dimensional space properly, and reduces dependence of recognition models on artificial tagging samples, and the recognition effect is superior to a representative human behavior recognition method based on linear discriminant analysis.

Description

technical field [0001] The invention relates to a pattern recognition and artificial intelligence technology, in particular to an accelerometer-based human behavior recognition method. Background technique [0002] Human action recognition is a complex problem that spans many disciplines and has received great attention in the field of industrial informatization. The basic steps include acquisition of perceptual signals, information processing and pattern classification. In recent years, many effective methods have been proposed to automatically recognize human actions. These methods can be classified into two categories: one is based on computer vision, and the other is based on accelerometers. Human behavior analysis systems based on computer vision do not work well in industrial environments because they are very sensitive to lighting conditions. In recent years, the application of accelerometer-based human behavior recognition in industrial environments has received m...

AI Technical Summary

Problems solved by technology

However, manual calibration of human behavior sample data is time-consuming and laborious

Using the linear discriminant analysis model for training requires a large number of manually labeled samples, which greatly increases the development cost of the human behavior model and requires a large number of manually labeled samples

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