Abstract:Aiming at the problem of limited resources such as smartphone CPU and storage in the research of smartphone-based human activity recognition, this paper proposes a lightweight stochastic configuration network human activity recognition model based on manifold regularization and QR decomposition. Firstly, Manifold Regularization is used to solve the problem of unpredictable nonlinear distribution after the input data are randomly mapped to the hidden layer space of SCNs, so as to improve the lightweight of the model structure. Secondly, QR decomposition is used to reduce the computational complexity of the output weights and further improve the lightweight of the model modeling process. Finally, based on two real human activity data sets, the effectiveness of the proposed model in model recognition accuracy and lightweight is evaluated. Experimental results show that, compared with SCNs, CNN, etc. the proposed model can not only improve the accuracy of activity recognition, but also effectively reduce the calculation complexity of output weights and improve the compactness of the model structure.