Title

Detecting Breathing Frequency and Maintaining a Proper Running Rhythm

Abstract

Running is a kind of whole body movement, which enables the whole body muscle rhythmic contraction and relaxation. A stable and harmonic running rhythm cannot only postpone runners' fatigue but also improve their exercise effectiveness. The paper presents an effective method of detecting runner's breathing frequency continuously and maintaining a stable running rhythm during running. Bluetooth headsets, smartphones and heart rate belts are utilized to obtain the sensed data, such as striding frequency, breathing frequency and heart rate. We propose a novel approach to calibrate the sensed data by integrating ambient sensed data with a physiological model called Locomotor Respiratory Coupling (LRC), which indicates possible ratios between the striding and breathing frequencies. In order to help the runner maintain a stable running rhythm, we use a proper music recommended by the server based on the history of the sensed data to encourage the runner to accelerate, decelerate or keep the running speed and breathe properly. Our method has been validated by extensive experiments and the experimental results indicate that it can accurately detect the breathing frequency and maintain a stable running rhythm for runners.

Department(s)

Computer Science

Research Center/Lab(s)

Intelligent Systems Center

Comments

This work was partly supported by the 973 Program (2013CB035503), National Natural Science Foundation of China (61572060, 61472024, 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.

Keywords and Phrases

Physiological models; Breathing frequency; Heart rates; Locomotor respiratory coupling (LRC); Running rhythm; Striding frequency; Heart

International Standard Serial Number (ISSN)

1574-1192

Document Type

Article - Journal

Document Version

Citation

File Type

text

Language(s)

English

Rights

© 2017 Elsevier, All rights reserved.

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