Indirect Estimation of Breathing Rate from Heart Rate Monitoring System during Running

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.

Concurrent Evolution of Biomechanical and Physiological Parameters With Running-Induced Acute Fatigue

“…Later, SDNN, SD2, pHF, pLF, LF/HF ratio, and CC are affected as well by acute fatigue. The decrease in HRV and HRC, accompanied with an increase in HF and pHF, support previous findings demonstrating the persistence of respiration sinus arrythmia (RSA) at high workloads (Blain et al, 2005;Prigent et al, 2021), as it is the main source of HRV at high exercise intensities.…”

Breath Tools: A Synthesis of Evidence-Based Breathing Strategies to Enhance Human Running

“…This phenomenon has been observed during incremental exercise ( Blain et al, 2005 ). Indeed, breathing may be the main mechanism responsible for short-term HR fluctuations, especially at higher intensities ( Bernardi et al, 1990 ; Prigent et al, 2021 ). Matsumoto et al (2011) tested this effect during exercise: longer exhales (33 vs. 50% dc) caused improved HRV, ventilatory efficiency ( V E /VCO 2 19.1 ± 2.9 vs. 22.1 ± 4.4), and VO 2 during incremental cycling.…”

Breathing Rate Estimation from Head-Worn Photoplethysmography Sensor Data Using Machine Learning

“…Such techniques most commonly entail spectral analysis of the frequencies contained in the respiratory signals. The spectral analysis consists of identifying the breathing frequency from a power spectrum calculated mainly using: fast Fourier transform (FFT) technique [ 18 , 19 , 20 ] and auto-regressive modelling [ 21 , 22 ]. In general, when using spectral analysis-based methods, the breathing rate is identified as the frequency corresponding to the spectral peak with the greatest magnitude in the range of 0.1–0.5 Hz, which is associated with spontaneous breathing rates.…”