Emerging evidence suggests that respiratory frequency (f_R) is a valid marker of physical effort. This has stimulated interest in developing devices that allow athletes and exercise practitioners to monitor this vital sign. The numerous technical challenges posed by breathing monitoring in sporting scenarios (e.g., motion artifacts) require careful consideration of the variety of sensors potentially suitable for this purpose. Despite being less prone to motion artifacts than other sensors (e.g., strain sensors), microphone sensors have received limited attention so far. This paper proposes the use of a microphone embedded in a facemask for estimating f_R from breath sounds during walking and running. f_R was estimated in the time domain as the time elapsed between consecutive exhalation events retrieved from breathing sounds every 30 s. Data were collected from ten healthy subjects (both males and females) at rest and during walking (at 3 km/h and 6 km/h) and running (at 9 km/h and 12 km/h) activities. The reference respiratory signal was recorded with an orifice flowmeter. The mean absolute error (MAE), the mean of differences (MOD), and the limits of agreements (LOAs) were computed separately for each condition. Relatively good agreement was found between the proposed system and the reference system, with MAE and MOD values increasing with the increase in exercise intensity and ambient noise up to a maximum of 3.8 bpm (breaths per minute) and −2.0 bpm, respectively, during running at 12 km/h. When considering all the conditions together, we found an MAE of 1.7 bpm and an MOD ± LOAs of −0.24 ± 5.07 bpm. These findings suggest that microphone sensors can be considered among the suitable options for estimating f_R during exercise.

Given the importance of respiratory frequency (f_R) as a valid marker of physical effort, there is a growing interest in developing wearable devices measuring f_R in applied exercise settings. Biosensors measuring chest wall movements are attracting attention as they can be integrated into textiles, but their susceptibility to motion artefacts may limit their use in some sporting activities. Hence, there is a need to exploit sensors with signals minimally affected by motion artefacts. We present the design and testing of a smart facemask embedding a temperature biosensor for f_R monitoring during cycling exercise. After laboratory bench tests, the proposed solution was tested on cyclists during a ramp incremental frequency test (RIFT) and high-intensity interval training (HIIT), both indoors and outdoors. A reference flowmeter was used to validate the f_R extracted from the temperature respiratory signal. The smart facemask showed good performance, both at a breath-by-breath level (MAPE = 2.56% and 1.64% during RIFT and HIIT, respectively) and on 30 s average f_R values (MAPE = 0.37% and 0.23% during RIFT and HIIT, respectively). Both accuracy and precision (MOD ± LOAs) were generally superior to those of other devices validated during exercise. These findings have important implications for exercise testing and management in different populations.

The breath-by-breath measurement of respiratory rate (RR) plays a pivotal role in sports and exercise. The accurate estimation of RR values on a breath-by-breath basis with wearable sensors has several open challenges during training, including motion artefacts and other breathingunrelated events. This paper presents a novel method based on a signal quality index (SQI) for identifying and excluding unreliable breaths from breathing waveforms. The method analyses the morphological characteristics of the respiratory signal, comparing each breath with an average breath template calculated as an average of all individual breaths. The comparison is made using a template matching without the need of a reference signal. Experimental tests have been carried out at rest and during walking, running, and cycling activities to assess the method’s performance in estimating breath-by-breath RR by comparison with reference values collected with a flowmeter. The comparison between the RR values has been performed with an ad hoc developed method able to accomplish this task, even when the number of breaths identified by the two devices is different. The obtained results showed that our SQI-based method improves the accuracy of RR estimation by reducing the Mean Absolute Percentage Error (MAPE) values in all the tested conditions (18.5%, 22.2%, 2.8% and 14.1% of MAPE improvement rate during rest, walking, running and cycling, respectively). Pilot tests during high-intensity interval training (HIIT) also demonstrated a 30.7% MAPE improvement rate. The promising findings demonstrated that using SQI-based algorithms can lead to more accurate RR estimations during exercise by using comfortable wearable sensors.

Music is an invaluable tool to improve affective valence during exercise, with the potential contribution of a mechanism called rhythmic entrainment. However, several methodological limitations impair our current understanding of the effect of music on relevant psychophysiological responses to exercise, including breathing variables. This study presents conceptual, methodological, and operational insight favoring the investigation of the effect of music on breathing during exercise. Three tools were developed for the quantification of the presence, degree, and magnitude of music-locomotor, locomotor-breathing, and music-breathing entrainment. The occurrence of entrainment was assessed during 30 min of moderate cycling exercise performed either when listening to music or not, and was complemented by the recording of relevant psychophysiological and mechanical variables. Respiratory frequency and expiratory time were among the physiological variables that were affected to a greater extent by music during exercise, and a significant (p < 0.05) music-breathing entrainment was found in all 12 participants. These findings suggest the importance of evaluating the effect of music on breathing responses to exercise, with potential implications for exercise prescription and adherence, and for the development of wearable devices simultaneously measuring music, locomotor, and breathing signals.

Growing evidence suggests that respiratory frequency (f_R) is a valid marker of effort during high-intensity exercise, including sports of an intermittent nature, like soccer. However, very few attempts have been made so far to monitor f_R in soccer with unobtrusive devices. This study assessed the validity of three strain-based commercial wearable devices measuring f_R during soccer-specific movements. On two separate visits to the soccer pitch, 15 players performed a 30 min validation protocol wearing either a ComfTech^® (CT) vest or a Bioharness^TM (BH) 3.0 strap and a Tyme Wear^TM (TW) vest. f_R was extracted from the respiratory waveform of the three commercial devices with custom-made algorithms and compared with that recorded with a reference face mask. The f_R time course of the commercial devices generally resembled that of the reference system. The mean absolute percentage error was, on average, 7.03% for CT, 8.65% for TW, and 14.60% for BH for the breath-by-breath comparison and 1.85% for CT, 3.27% for TW, and 7.30% for BH when comparison with the reference system was made in 30 s windows. Despite the challenging measurement scenario, our findings show that some of the currently available wearable sensors are indeed suitable to unobtrusively measure f_R in soccer.

Respiratory rate (RR) measurement has numerous implications for testing and training monitoring both in research and applied settings. Cycling is among the exercise modalities that may benefit the most from the continuous RR monitoring. The substantial advances in technological development recently observed in the field of respiratory monitoring offer new opportunities for RR measurement in applied settings. This study assessed the performance of a measurement system based on a temperature (i.e., a thermistor) sensor embedded in a printed circuit board in estimating RR during a cycling incremental exercise test. The printed circuit board was physically installed at the end of the face mask of a metabolic cart (reference system) commonly used in exercise testing. The reference device was based on airflow measurement and used with the purpose of validating RR estimated from the temperature sensor. A frequency domain analysis was implemented to estimate RR from a windowed signal with an update time of 1 s. Five window sizes have been tested (i.e., 5 s, 10 s, 20 s, 30 s, and 60 s). Per each condition the mean absolute error (MAE), the mean absolute percentage error (MAPE) and the Bland-Altman analysis were used as comparison metrics. Results demonstrated lower MAE and MAPE in the case of higher window size, with the worst results found analyzing data with 5 s window size (MAE < 1.2 bpm, MAPE < 2.8%). The promising results encourage additional tests and refinement of the measuring system for its use in sports applications.