Activity recognition based on adaptive window and broad learning

With the widespread use of sensing elements in commercial equipment, action recognition technology is required to be more practical in people's life, especially the stable and accurate recognition. Among them, using sliding window for motion perception is an effective recognition method. However, most of the current recognition models are designed for a single action, which not only has poor recognition stability, but also cannot effectively recognize the action. This paper presents a method of action recognition based on adaptive window and broad learning, and designs an action recognition system EVM, the system effectively preprocesses the action data and realizes the accurate recognition of actions. Firstly, EVM smooth the source action data. Then, this paper proposes an extreme value filtering method to avoid the interference of peak/valley extreme points and ensures the effectiveness of action division through the adaptive window. Finally, a recognition model based on broad learning is used to classify action behaviors. According to the comparison and verification of a large number of experiments, the EVM system has a recognition accuracy as high as 97.91%, which is much better and faster than the CNN model.