Next Article in Journal
A Novel Vehicle Detection Method Based on the Fusion of Radio Received Signal Strength and Geomagnetism
Next Article in Special Issue
General Architecture for Development of Virtual Coaches for Healthy Habits Monitoring and Encouragement
Previous Article in Journal
Riemannian Spatio-Temporal Features of Locomotion for Individual Recognition
Previous Article in Special Issue
Towards Inertial Sensor Based Mobile Gait Analysis: Event-Detection and Spatio-Temporal Parameters
Open AccessArticle

Empirical Study and Improvement on Deep Transfer Learning for Human Activity Recognition

School of Computer Science and Technology, Harbin Institute of Technology, Harbin 150001, China
*
Author to whom correspondence should be addressed.
These authors contributed equally to this work.
Sensors 2019, 19(1), 57; https://doi.org/10.3390/s19010057
Received: 29 October 2018 / Revised: 21 December 2018 / Accepted: 21 December 2018 / Published: 24 December 2018
(This article belongs to the Special Issue Wireless Body Area Networks and Connected Health)
Human activity recognition (HAR) based on sensor data is a significant problem in pervasive computing. In recent years, deep learning has become the dominating approach in this field, due to its high accuracy. However, it is difficult to make accurate identification for the activities of one individual using a model trained on data from other users. The decline on the accuracy of recognition restricts activity recognition in practice. At present, there is little research on the transferring of deep learning model in this field. This is the first time as we known, an empirical study was carried out on deep transfer learning between users with unlabeled data of target. We compared several widely-used algorithms and found that Maximum Mean Discrepancy (MMD) method is most suitable for HAR. We studied the distribution of features generated from sensor data. We improved the existing method from the aspect of features distribution with center loss and get better results. The observations and insights in this study have deepened the understanding of transfer learning in the activity recognition field and provided guidance for further research. View Full-Text
Keywords: human activity recognition; transfer learning; deep learning; sensor data human activity recognition; transfer learning; deep learning; sensor data
Show Figures

Figure 1

MDPI and ACS Style

Ding, R.; Li, X.; Nie, L.; Li, J.; Si, X.; Chu, D.; Liu, G.; Zhan, D. Empirical Study and Improvement on Deep Transfer Learning for Human Activity Recognition. Sensors 2019, 19, 57.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop