Research On User's Behavior Identification Based On WSN

In recent years,the progress of AI technology has promoted the development and application of smart home.In smart home system,wireless sensor network technology has the advantages of flexible deployment and convenience.It plays an important role in the field of user 's behavior recognition.By deploying wireless sensor network technology,collected user's daily information,captured the habits of user's behavior,and analyzed the user 's health condition.In the deployment and application of this technology,it's very important to build an effective model of the monitored persons.In order to improve the accuracy and reliability of precise monitoring and analysis,a new behavior model and an improved algorithm are proposed in this thesis.A new SVM/HMM model,which combined the characteristics of the multi-classification of support vector machine and the double random process of the hidden Markov model,used the Baum-Welch algorithm to establish the relationship between the output of sensors and user's behavior.In order to solve the problem that Baum-Welch algorithm is sensitive to initial parameters,the genetic algorithm is used to optimize global search in this thesis.Used the idea of chaotic map to solve the premature phenomenon in the process of mutation,which effectively solved the problem that search results are easy to fall into local optimization.Finally,the public Kastern data and OrdonezA data are used to compare the recognition results between the new method and the traditional method.In the simulation process,the discrete empty data is marked to reduced the redundancy of data.The simulation results show that the method of user's behavior recognition based on SVM/HMM model is better than the traditional recognition method.