Abstract
Recent advances in wearable technologies integrating multi-modal sensors have enabled the in-field monitoring of several physiological metrics. In sport applications, wearable devices have been widely used to improve performance while minimizing the risk of injuries and illness. The objective of this project is to estimate breathing rate (BR) from respiratory sinus arrhythmia (RSA) using heart rate (HR) recorded with a chest belt during physical activities, yielding additional physiological insight without the need of an additional sensor. Thirty-one healthy adults performed a run at increasing speed until exhaustion on an instrumented treadmill. RR intervals were measured using the Polar H10 HR monitoring system attached to a chest belt. A metabolic measurement system was used as a reference to evaluate the accuracy of the BR estimation. The evaluation of the algorithms consisted of exploring two pre-processing methods (band-pass filters and relative RR intervals transformation) with different instantaneous frequency tracking algorithms (short-term Fourier transform, single frequency tracking, harmonic frequency tracking and peak detection). The two most accurate BR estimations were achieved by combining band-pass filters with short-term Fourier transform, and relative RR intervals transformation with harmonic frequency tracking, showing 5.5% and 7.6% errors, respectively. These two methods were found to provide reasonably accurate BR estimation over a wide range of breathing frequency. Future challenges consist in applying/validating our approaches during in-field endurance running in the context of fatigue assessment.
Highlights
Recent advances in wearable technologies that integrate multi-modal sensors have enabled the in-field monitoring of several physiological metrics
The minimum and maximum breathing rate (BR) obtained from the reference system were 8.5 ± 2.4 bpm and 71.5 ± 15.6 bpm, respectively
In order to remove the long-range trend and non-respiratory frequency components, the RR time-series was filtered using the pre-processing methods described in Section 2.3signal
Summary
Recent advances in wearable technologies that integrate multi-modal sensors have enabled the in-field monitoring of several physiological metrics. It was shown that BR is closely associated with the subjects’ perceived exertion during an activity [5], as well as being sensitive to different fatigue states [11,12]. This physiological variable has a fast response at exercise onset/offset and changes rapidly in proportion to workload variations [5]. This makes BR a useful variable to monitor during training, as a marker for physical efforts and recovery
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