Abstract: With the popularity of wearable devices such as smart watches and bracelets,using them for human activity recognition and decoding human behavior is of great significance for health monitoring,daily behavior analysis,smart home and other applications.However,traditional action recognition algorithms have problems such as difficult feature extraction and low recognition accuracy,and are all based on the close set assumption,that is,all training data and test data come from the same label space,while most of the real world is open.In the open-set scene,unknown label samples may be sent to the model during the test phase,resulting in incorrect classification.This paper proposes a multi-channel adaptive convolutional network(MCACN) for human acti-vity recognition.For the problem that the traditional CNN network feature extraction is limited to a small range,the adaptive convolution module can use convolution kernels of different sizes to extract features of different time spans,automatically calculate the weights and sum them up.In addition,the multi-channel structure of MCACN enables each sensor data to be processed separately to obtain feature details that can distinguish similar actions.Finally,this paper designs a label-based multivariate variational autoencoder,and proposes MCACN-VAE for open set recognition.The model can identify unknown classes by calculating recons-truction loss,focusing on known class actions,and improving the robustness of the model.Experimental results show that in the closed set experiment,the MCACN model can effectively recognize the actions,and the accuracy of the recognition of seven daily actions has reached more than 91%,the overall accuracy has reached 95%.In the open set experiment,the overall recognition accuracy of MCACN-VAE for known categories has reached more than 89% at different degrees of openness,and the recognition accuracy of unknown action segments has also remained above 75%.It proves that the proposed model can effectively reject unknown classes and identify known classes.

Key words: Wearable devices, Activity recognition, Adaptive convolution, Open set recognition