Research Article
The Empirical Study On Deep Convolutional Network Transferring Among Users Within Activity Recognition
@INPROCEEDINGS{10.4108/eai.21-6-2018.2276633, author={Jiazhen Li and Renjie Ding and Lanshun Nie and Xue Li and Xiandong Si and Lulu Wang and Dianhui Chu and Dechen Zhan}, title={The Empirical Study On Deep Convolutional Network Transferring Among Users Within Activity Recognition}, proceedings={11th EAI International Conference on Mobile Multimedia Communications}, publisher={EAI}, proceedings_a={MOBIMEDIA}, year={2018}, month={9}, keywords={sensor-based data human activity recognition cnn transfer learning}, doi={10.4108/eai.21-6-2018.2276633} }- Jiazhen Li
Renjie Ding
Lanshun Nie
Xue Li
Xiandong Si
Lulu Wang
Dianhui Chu
Dechen Zhan
Year: 2018
The Empirical Study On Deep Convolutional Network Transferring Among Users Within Activity Recognition
MOBIMEDIA
EAI
DOI: 10.4108/eai.21-6-2018.2276633
Abstract
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.
