Mechanical and electromyographic responses during the 3-min all-out test in competitive cyclists

Respiratory Rate Estimation during Walking and Running Using Breathing Sounds Recorded with a Microphone

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.

Design and Testing of a Smart Facemask for Respiratory Monitoring during Cycling 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 Importance of Respiratory Rate Monitoring: From Healthcare to Sport and Exercise

Respiratory rate is a fundamental vital sign that is sensitive to different pathological conditions (e.g., adverse cardiac events, pneumonia, and clinical deterioration) and stressors, including emotional stress, cognitive load, heat, cold, physical effort, and exercise-induced fatigue. The sensitivity of respiratory rate to these conditions is superior compared to that of most of the other vital signs, and the abundance of suitable technological solutions measuring respiratory rate has important implications for healthcare, occupational settings, and sport. However, respiratory rate is still too often not routinely monitored in these fields of use. This review presents a multidisciplinary approach to respiratory monitoring, with the aim to improve the development and efficacy of respiratory monitoring services. We have identified thirteen monitoring goals where the use of the respiratory rate is invaluable, and for each of them we have described suitable sensors and techniques to monitor respiratory rate in specific measurement scenarios. We have also provided a physiological rationale corroborating the importance of respiratory rate monitoring and an original multidisciplinary framework for the development of respiratory monitoring services. This review is expected to advance the field of respiratory monitoring and favor synergies between different disciplines to accomplish this goal.

Respiratory Frequency during Exercise: The Neglected Physiological Measure

The use of wearable sensor technology for athlete training monitoring is growing exponentially, but some important measures and related wearable devices have received little attention so far. Respiratory frequency (f_R), for example, is emerging as a valuable measurement for training monitoring. Despite the availability of unobtrusive wearable devices measuring f_R with relatively good accuracy, f_R is not commonly monitored during training. Yet f_R is currently measured as a vital sign by multiparameter wearable devices in the military field, clinical settings, and occupational activities. When these devices have been used during exercise, f_R was used for limited applications like the estimation of the ventilatory threshold. However, more information can be gained from f_R. Unlike heart rate, V˙O₂, and blood lactate, f_R is strongly associated with perceived exertion during a variety of exercise paradigms, and under several experimental interventions affecting performance like muscle fatigue, glycogen depletion, heat exposure and hypoxia. This suggests that f_R is a strong marker of physical effort. Furthermore, unlike other physiological variables, f_R responds rapidly to variations in workload during high-intensity interval training (HIIT), with potential important implications for many sporting activities. This Perspective article aims to (i) present scientific evidence supporting the relevance of f_R for training monitoring; (ii) critically revise possible methodologies to measure f_R and the accuracy of currently available respiratory wearables; (iii) provide preliminary indication on how to analyze f_R data. This viewpoint is expected to advance the field of training monitoring and stimulate directions for future development of sports wearables.

Decrease of muscle fiber conduction velocity correlates with strength loss after an endurance run

Monitoring surface electromyographic (EMG) signals can provide useful insights for characterizing muscle fatigue, which is defined as an exercise-induced strength loss. This experiment investigated the muscle fiber conduction velocity (CV) changes induced by an endurance run. The day before and immediately after a half-marathon run (21.097 km) 11 amateur runners performed maximum voluntary contractions (MVCs) of knee extensor muscles. During the MVC, multichannel EMG was recorded from the vastus lateralis and EMG amplitude and CV were calculated. After the run, knee extensors showed a decreased strength (-13  ±  9%, p  =  0.001) together with a reduction in EMG amplitude (-13  ±  10%, p  =  0.003) and in CV (-6  ±  8%, p  =  0.032). Knee extensor strength loss positively correlated with vastus lateralis CV differences (r  =  0.76, p  =  0.006). Thus, the exercises-induced muscle fatigue was associated not only with a decrease in EMG amplitude, but also with a reduction in CV. This finding suggests that muscle fibers with higher CV (i.e. those with greater fiber size) were the most impaired during strength production after an endurance run.