Human Activity Recognition Based on Features Fusion / 医用生物力学

ABSTRACT

Objective To establish a human activity recognition （HAR）model based on human activity signals obtained by built-in sensors of the mobile phone, so as to support daily physical state assessment, special population monitoring and other biomedical researches. Methods The mobile signal was collected using the mobile phone built-in sensor, and the public data set UCI HAR and WISDM were used as experimental data. The HAR model was established by using the feature extraction method combined with convolutional neural network and autoregressive model. Results The models all achieved more than 90% recognition accuracy in the self-collected dataset, UCI HAR and WISDM. Conclusions The introduction of autoregressive model can avoid the manual design eigenvalues and effectively reduce the computational complexity of large-scale stacked convolutional layers. The research findings prove that the method based on feature fusion can effectively recognize human activity.