In the field of human behavior recognition based on wearable sensors

extracting effective features of original data and establishing suitable classification model are the keys to improve recognition accuracy.In view of the above problems

an improved deep convolutional neural network（DCNN）model is proposed.This method adds signal fusion unit to the classic DCNN model

and propose a method for converting time series into single-channel behavior picture.After being processed by signal fusion unit

the behavior pictures composed of the acceleration signal

angular velocity signal

and pitch angle signal can realize cross-channel information fusion

and then extract the tensor characteristics of the behavior picture to achieve the recognition of six kinds of daily behaviors like walking

running

sitting

lying

falling and jumping. Experiments show that the recognition rate of this method on the UCI open source dataset reaches 97.05 %

which is higher than that of traditional classification models.