Machine learning-based wireless sensing motion identification method

Abstract

The invention discloses a machine learning-based wireless sensing motion identification method which comprises the following steps: a step of data collection, a step of data denoising operation, a step of feature extraction and a step of SVM model training and identifying operation. During data collection operation, absolute value of a group of CSI data collected on each sampling point is obtained and is read into a 30*Nr*Nt matrix form. A PCA mode is mainly adopted for the data denoising operation. Feature extraction operation can be conducted based on discrete wavelet transformation. To make SVM model training convenient, training samples are subjected to Kmeans clustering operation via the machine learning-based wireless sensing motion identification method, n clustering centers can be used as word bags, and voting operation is performed based on feature vectors and best matching items of all the word bags; when the matrix form feature vectors are converted into column vectors, SVM model training can be realized conveniently. The machine learning-based wireless sensing motion identification method is a human body behavior identification method which is high in identification accuracy and high in robustness for environment change.

Description

technical field [0001] The invention belongs to the field of artificial intelligence technology, and in particular relates to a machine learning-based wireless perception behavior recognition method. Background technique [0002] In today's society, with the continuous development of information technology, as an important research field in artificial intelligence technology, the development of human behavior recognition technology has become a key technology to promote progress in fields such as health care, smart home, and health status tracking. Important position. Traditional human motion recognition technology mainly uses camera equipment, radar or some wearable sensor equipment. However, vision-based methods are limited by user privacy and lighting conditions. Operation with radar is limited by its operating range. However, some wearable sensor devices are not portable enough and inconvenient to use. At the same time, with the development of society and the increas...

AI Technical Summary

Problems solved by technology

However, a quantitative feature is not proposed to describe the relationship between CSI data and different human actions, so that the designed action recognition model shows good robustness in different environments.

Moreover, the traditional low-pass filter and medium-pass filter used in most researches to denoise the collected CSI data, the denoising effect is not particularly ideal, and better denoising technology is needed

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