An Efficient Method of Detecting Breathing Frequency While Running
Abstract
Breathing plays an important role in the process of running. A stable and harmonic breathing rhythm can postpone runners' fatigue and help to improve their running performances. This paper presents a method that can detect runner's breathing frequency continuously. We utilize Bluetooth headset and smart phone to obtain sensed data, such as striding frequency and breathing frequency. Due to the interference of ambient noise, the detection will be inaccurate. In order to cope with this problem, we calibrate the detection result by leveraging a physiological model, called Locomotor Respiratory Coupling (LRC), which indicates possible ratios between the stride and breathing frequencies. Our method has been validated by extensive experiments and the experimental results indicate that it can accurately detect the breathing frequency for runners.
Recommended Citation
F. Gu et al., "An Efficient Method of Detecting Breathing Frequency While Running," Proceedings of the 2016 IEEE International Conference on Smart Computing (2016, St. Louis, MO), Institute of Electrical and Electronics Engineers (IEEE), May 2016.
The definitive version is available at https://doi.org/10.1109/SMARTCOMP.2016.7501677
Meeting Name
2016 IEEE International Conference on Smart Computing, SMARTCOMP 2016 (2016: May 18-20, St. Louis, MO)
Department(s)
Computer Science
Research Center/Lab(s)
Intelligent Systems Center
Keywords and Phrases
Smartphones; A-stable; Ambient noise; Bluetooth headsets; Physiological models
International Standard Book Number (ISBN)
978-1-5090-0898-8
Document Type
Article - Conference proceedings
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2016 Institute of Electrical and Electronics Engineers (IEEE), All rights reserved.
Publication Date
01 May 2016
Comments
This work was partly supported by the 973 Program (2013CB035503), National Natural Science Foundation of China (61572060, 61472024, 61170296 and 61190125), and the R&D Program (2013BAH35F01). The work of S. K. Das was partially supported by the US NSF grants under award numbers IIS-1404673, IIP-1540119, CNS-1355505 and CNS- 1404677.