Research On CSI Identification Of Indoor Teaching Behavior Based On Multi-Feature Fusion

Teaching behavior recognition has a wide range of applications in smart classroom and is one of the important methods of educational intelligence.At present,the widely used behavior recognition methods are mainly divided into two categories: camera-based behavior recognition and special sensor-based behavior recognition.Camera-based identification methods have potential privacy risks,and dedicated sensors cannot be used in real classrooms due to their high cost and discomfort.With CSI(Channel State Information)of Wi-Fi signal,behavior recognition can be performed at a low cost and completely avoid privacy risks.In the process of indoor teaching behavior recognition based on CSI,due to the sensitivity of CSI to the experimental environment and human physiological characteristics,any changes in experimental settings will affect the recognition results.In order to improve the performance of using CSI to realize indoor teaching behavior recognition in complex scenes,in view of the change of experimental settings,this paper eliminates background noise to avoid the influence of experimental environment on CSI,and proposes an indoor teaching behavior recognition algorithm based on multi-feature fusion M-LSTM.The algorithm uses a multi-layer LSTM network to extract local time series features,and fuses them with the global features obtained by statistics and sends them to the attention mechanism module,which improves the feature extraction ability of the model in complex environments.The model cannot recognize new users,and it is difficult to obtain a large number of label samples from new users in a short time.The paper builds an identification algorithm for new users based on SSGAN,and samples of new users are transferred to public datasets through Cycle GAN,so as to obtain unlabeled samples with information about the physiological characteristics of new users.M-LSTM outputs and inputs are modified as the discriminator of SSGAN,and the recognition performance for new users of the model is improved through semi-supervised learning.On different experimental datasets,training and validation results show that both recognition models significantly improve their ability to recognize CSI in complex environments as well as recognize new users.