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Mobile Information Systems
Volume 2018 (2018), Article ID 5214067, 9 pages
Research Article

Deep Learning versus Professional Healthcare Equipment: A Fine-Grained Breathing Rate Monitoring Model

Big Data Research Center, Department of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, China

Correspondence should be addressed to Xili Dai; moc.361@sc_ilixiad

Received 28 July 2017; Revised 13 November 2017; Accepted 28 November 2017; Published 1 March 2018

Academic Editor: Pino Caballero-Gil

Copyright © 2018 Bang Liu et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


In mHealth field, accurate breathing rate monitoring technique has benefited a broad array of healthcare-related applications. Many approaches try to use smartphone or wearable device with fine-grained monitoring algorithm to accomplish the task, which can only be done by professional medical equipment before. However, such schemes usually result in bad performance in comparison to professional medical equipment. In this paper, we propose DeepFilter, a deep learning-based fine-grained breathing rate monitoring algorithm that works on smartphone and achieves professional-level accuracy. DeepFilter is a bidirectional recurrent neural network (RNN) stacked with convolutional layers and speeded up by batch normalization. Moreover, we collect 16.17 GB breathing sound recording data of 248 hours from 109 and another 10 volunteers to train and test our model, respectively. The results show a reasonably good accuracy of breathing rate monitoring.