Based On Time Series Analysis Of Human Movement Behavior Pattern Recognition Research

AS a part of artificial intelligence and intelligent home field research,human motion recognition has been paid more and more attention by people from all walks of life both at home and abroad.The development of communication network makes the Internet of Things technology as a transmission medium to achieve intelligent home,intelligent medical care,health care and rehabilitation,help the elderly disabled and other aspects of human-computer interaction to a new height,but it is only to achieve human-computer interaction in the middle one Ring,can not capture the actual movement of human information.Human motion recognition is an important manifestation of human-computer interaction control.It is an important research subject that how to collect effective information of human body movement to achieve human-computer interactive control through the transmission medium and then to monitor and recognize human daily activities in real time.With the development of embedded technology such as microelectronics,communication technology,SoC,semiconductor technology and integrated circuit,the acceleration sensor integrated with wireless communication module has been applied to the field of human motion real-time acquisition and monitoring and identification.At the same time,There are many practical problems to be improved and resolved.Based on this,this paper presents a research on human motion recognition based on time series analysis,which is based on the research of human motion recognition,such as acquisition of human acceleration signal,signal preprocessing,time series feature extraction,model training recognition and so on.The main research work is as follows:In order to meet the requirements of time series analysis,this paper designs a specific accelerometer data acquisition platform,and establishes a set of data sets for various movements of daily human motion,which is an important basis and prerequisite for the study.The gravity component of the human acceleration signal is removed by using the first order low-pass filter with frequency of 5Hz.The results show that the gravity component can be effectively correlated with the gravity component of the human body acceleration signal by using this method.Separating the Acceleration Components Characterizing Human Motion.In this paper,an improved wavelet threshold de-noising algorithm is proposed based on the defects of traditional wavelet threshold denoising algorithm,which solves the problems such as oscillation,fixed attenuation and smoothness of traditional wavelet threshold denoising algorithm.Filtering the collected acceleration signal in the purpose of the noise signal.This paper presents a method of feature extraction for acceleration time series analysis based on the characteristics of the acceleration time series formed by the real-time change of the human body's motion acceleration signal with the movement state of the human body.(HMM).The results show that this method can quickly and effectively differentiate the various static motion patterns of human daily life.According to the advantages of the LSTM long-short-term memory model in dealing with time series problems,we introduce the LSTM model into the training and identification of human motion recognition system.The results show that this model can learn more complete time series of human motion acceleration.Relevance information,which can effectively identify the transfer process between different behavior patterns of human daily life,and further validate the feasibility of the proposed human motion recognition system.