In order to solve the difficulty of feature extraction from raw signals of inertial sensors in human activity recognition(HAR) task

a RepVGG human activity recognition network model optimized by variational mode decomposition(VMD)and multiscale channel attention(MCA) mechanism is proposed.Firstly

the model uses VMD to decompose the raw signals of inertial sensors into several intrinsic mode functions(IMFs)according to different center frequencies.Next

these IMFs are further optimized using the channel attention mechanism to highlight features that contribute to enhancing the recognition precision.Finally

feature extraction and activity classification are performed on the optimized IMFs using RepVGG

an efficient convolutional neural network with structural re-parameterization.Experimental results show that the model achieves an accuracy of 97.42% on the UCI-HAR dataset.This model will provide reliable technical support for activity recognition in different scenarios.