The Empirical Study On Deep Convolutional Network Transferring Among Users Within Activity Recognition

Human activity recognition based on sensor data is one of the significant problems in pervasive computing. In recent years, deep learning has become the main method in this field due to its high accuracy. However, it’s difficult to recognize activities of user B with the model trained for user A. The effect of transferring the model (among different users) is the key that restricts activity recognition in practice. At present, there is still little research on the transferring of deep learning model in this field. Its effect, principle and influencial factors remain to be studied. We carried out the empirical study on the transferring of deep learning model among users. We visualized the features extracted from CNN and studied its distribution. We compared the feasibility, strength and weakness of typical unsupervised and semi-supervised transferring methods. The observations and insights in this study have deepened the understanding of transferring in activity recognition field and provide guidance for further research.