A computational predictor of the anaerobic mechanical power outputs from a clinical exercise stress test.

We previously were able to predict the anaerobic mechanical power outputs using features taken from a maximal incremental cardiopulmonary exercise stress test (CPET). Since a standard aerobic exercise stress test (with electrocardiogram and blood pressure measurements) has no gas exchange measurement and is more popular than CPET, our goal, in the current paper, was to investigate whether features taken from a clinical exercise stress test (GXT), either submaximal or maximal, can predict the anaerobic mechanical power outputs to the same level as we found with CPET variables. We have used data taken from young healthy subjects undergoing CPET aerobic test and the Wingate anaerobic test, and developed a computational predictive algorithm, based on greedy heuristic multiple linear regression, which enabled the prediction of the anaerobic mechanical power outputs from a corresponding GXT measures (exercise test time, treadmill speed and slope). We found that for submaximal GXT of 85% age predicted HRmax, a combination of 3 and 4 variables produced a correlation of r = 0.93 and r = 0.92 with % error equal to 15 ± 3 and 16 ± 3 on the validation set between real and predicted values of the peak and mean anaerobic mechanical power outputs (p < 0.001), respectively. For maximal GXT (100% of age predicted HRmax), a combination of 4 and 2 variables produced a correlation of r = 0.92 and r = 0.94 with % error equal to 12 ± 2 and 14 ± 3 on the validation set between real and predicted values of the peak and mean anaerobic mechanical power outputs (p < 0.001), respectively. The newly developed model allows to accurately predict the anaerobic mechanical power outputs from a standard, submaximal and maximal GXT. Nevertheless, in the current study the subjects were healthy, normal individuals and therefore the assessment of additional subjects is desirable for the development of a test applicable to other populations

Facial electromyography during exercise using soft electrode array: A feasibility study.

The use of wearable sensors for real-time monitoring of exercise-related measures has been extensively studied in recent years (e.g., performance enhancement, optimizing athlete's training, and preventing injuries). Surface electromyography (sEMG), which measures muscle activity, is a widely researched technology in exercise monitoring. However, due to their cumbersome nature, traditional sEMG electrodes are limited. In particular, facial EMG (fEMG) studies in physical training have been limited, with some scarce evidence suggesting that fEMG may be used to monitor exercise-related measurements. Altogether, sEMG recordings from facial muscles in the context of exercise have been examined relatively inadequately. In this feasibility study, we assessed the ability of a new wearable sEMG technology to measure facial muscle activity during exercise. Six young, healthy, and recreationally active participants (5 females), performed an incremental cycling exercise test until exhaustion, while facial sEMG and vastus lateralis (VL) EMG were measured. Facial sEMG signals from both natural expressions and voluntary smiles were successfully recorded. Stable recordings and high-resolution facial muscle activity mapping were achieved during different exercise intensities until exhaustion. Strong correlations were found between VL and multiple facial muscles' activity during voluntary smiles during exercise, with statistically significant coefficients ranging from 0.80 to 0.95 (p<0.05). This study demonstrates the feasibility of monitoring facial muscle activity during exercise, with potential implications for sports medicine and exercise physiology, particularly in monitoring exercise intensity and fatigue

Thermographic Changes following Short-Term High-Intensity Anaerobic Exercise

Current studies report thermographic changes following aerobic or resistance exercise but not short, vigorous anaerobic exercise. Therefore, we investigated body surface temperature changes using thermal imaging following a short session of anaerobic exercise. We studied three different regions of interest (ROIs): the legs, chest, and forehead. Thermal imaging for each participant was performed before and immediately after completing a Wingate anaerobic test and every minute during a 15 min recovery period. Immediately after the test, the maximum temperature was significantly higher in all ROIs (legs, p = 0.0323; chest, p = 0.0455; forehead, p = 0.0444) compared to pre-test values. During the recovery period, both legs showed a significant and continuous temperature increase (right leg, p = 0.0272; left leg, p = 0.0382), whereas a non-significant drop was noted in the chest and forehead temperatures. Additionally, participants with a lower anaerobic capacity exhibited a higher delta increase in surface leg temperature than participants with higher anaerobic capacities, with a minimal change in surface leg temperature. This is the first study to demonstrate body surface temperature changes following the Wingate anaerobic test. This temperature increase is attributed to the high anaerobic mechanical power outputs achieved by the leg muscles and the time taken for temperature reduction post-exercise

Facial EMG channels and corresponding facial regions (lower face—LF, upper face—UF) and monitored location.

Facial EMG channels and corresponding facial regions (lower face—LF, upper face—UF) and monitored location.</p

Facial sEMG during different exercise intensities.

Capture of facial expression (a); Raw signal from the zygomaticus major muscle (Ch8) (b); The estimated Wlech’s power spectral density (PSD) (c); Corresponding accelerometer and gyroscope 3-axis data (d and e).</p

Experimental setup.

The sEMG multi-electrode array sticker placement on a participant, with the channel numbers corresponding to each electrode, in both upper and lower facial regions. Also shown are the VL EMG unit and the DAU supporting 16-channel facial EMG recording and acceleration data collection of the head.</p

Neutral facial expression ratio (NFER) calculated for all 16 channels during the test, indicating signal’s stability throughout the intensity range (N = 6).

Neutral facial expression ratio (NFER) calculated for all 16 channels during the test, indicating signal’s stability throughout the intensity range (N = 6).</p