Multiscale Bidirectional Input Convolutional and Deep Neural Network for Human Activity Recognition

In this paper, we proposed a multiscale and bidirectional input model based on convolutional neural network and deep neural network, named MBCDNN. In order to solve the problem of inconsistent activity segments, a multiscale input module is constructed to make up for the noise caused by filling. In order to solve the problem that single input is not enough to extract features from original data, we propose to manually design aggregation features combined with forward sequence and reverse sequence and use five cross-validation and stratified sampling to enhance the generalization ability of the model. According to the particularity of the task, we design an evaluation index combined with scene and action weight, which enriches the learning ability of the model to a great extent. In the 19 kinds of activity data based on scene+action, the accuracy and robustness are significantly improved, which is better than other mainstream traditional methods.