The relationship between blood potassium, blood lactate, and electromyography signals related to fatigue in a progressive cycling exercise test

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.

Curcuma longa L. Water Extract Enhances Endurance Exercise Capacity by Promoting Intramuscular Mitochondrial Biogenesis in Mice

We investigated the effect of Curcuma longa L. extract on endurance exercise capacity (EEC). EEC is the ability to exercise continuously and recover quickly, even when tired. C. longa contains antioxidants that contribute beneficial effects on the body. We separated groups of nonexercise (CON), exercise control (Ex-CON), branched-chain amino acid (BCAA) intake, and C. longa water extract (CLW) intake (Ex-CLW). EEC increased on the 28th day of BCAA and CLW intake. Both treatment groups exhibited decreased lactate levels with increased levels of nonesterified fatty acids and muscular glycogen compared with the Ex-CON group. Also, the Ex-CLW group possessed higher intramuscular antioxidant enzyme activities (catalase, superoxide dismutase, and glutathione peroxidase) than the Ex-CON group. The expression of PGC-1α, NRF, and Tfam, which are factors related to mitochondrial biogenesis, increased in the Ex-CLW group. Results suggest that CLW intake elevated EEC by increasing intramuscular mitochondrial biogenesis through suppressing the accumulation of fatigue substances and increasing fat consumption, and antioxidant enzyme activity.

A Novel Method for Classification of Running Fatigue Using Change-Point Segmentation

Blood lactate accumulation is a crucial fatigue indicator during sports training. Previous studies have predicted cycling fatigue using surface-electromyography (sEMG) to non-invasively estimate lactate concentration in blood. This study used sEMG to predict muscle fatigue while running and proposes a novel method for the automatic classification of running fatigue based on sEMG. Data were acquired from 12 runners during an incremental treadmill running-test using sEMG sensors placed on the vastus-lateralis, vastus-medialis, biceps-femoris, semitendinosus, and gastrocnemius muscles of the right and left legs. Blood lactate samples of each runner were collected every two minutes during the test. A change-point segmentation algorithm labeled each sample with a class of fatigue level as (1) aerobic, (2) anaerobic, or (3) recovery. Three separate random forest models were trained to classify fatigue using 36 frequency, 51 time-domain, and 36 time-event sEMG features. The models were optimized using a forward sequential feature elimination algorithm. Results showed that the random forest trained using distributive power frequency of the sEMG signal of the vastus-lateralis muscle alone could classify fatigue with high accuracy. Importantly for this feature, group-mean ranks were significantly different (p < 0.01) between fatigue classes. Findings support using this model for monitoring fatigue levels during running.

A Comparative Study of EMG Indices in Muscle Fatigue Evaluation Based on Grey Relational Analysis during All-Out Cycling Exercise

The increased popularization of cycling has brought an increase in cycling-related injuries, which has been suggested to be associated with muscle fatigue. However, it still remains unclear on the utility of different EMG indices in muscle fatigue evaluation induced by cycling exercise. In this study, ten cyclist volunteers performed a 30-second all-out cycling exercise after a warm-up period. Surface electromyography (sEMG) from vastus lateralis muscle (VL) and power output and cadence were recorded and EMG RMS, MF and MPF based on Fourier Transform, MDF and MNF based on wavelet packet transformation, and C(n) based on Lempel–Ziv complexity algorithm were calculated. Utility of the indices was compared based on the grey rational grade of sEMG indices and power output and cadence. The results suggested that MNF derived from wavelet packet transformation was significantly higher than other EMG indices, indicating the potential application for fatigue evaluation induced by all-out cycling exercise.

Electromyographic Patterns during Golf Swing: Activation Sequence Profiling and Prediction of Shot Effectiveness

This study analyzes muscle activity, recorded in an eight-channel electromyographic (EMG) signal stream, during the golf swing using a 7-iron club and exploits information extracted from EMG dynamics to predict the success of the resulting shot. Muscles of the arm and shoulder on both the left and right sides, namely flexor carpi radialis, extensor digitorum communis, rhomboideus and trapezius, are considered for 15 golf players (∼5 shots each). The method using Gaussian filtering is outlined for EMG onset time estimation in each channel and activation sequence profiling. Shots of each player revealed a persistent pattern of muscle activation. Profiles were plotted and insights with respect to player effectiveness were provided. Inspection of EMG dynamics revealed a pair of highest peaks in each channel as the hallmark of golf swing, and a custom application of peak detection for automatic extraction of swing segment was introduced. Various EMG features, encompassing 22 feature sets, were constructed. Feature sets were used individually and also in decision-level fusion for the prediction of shot effectiveness. The prediction of the target attribute, such as club head speed or ball carry distance, was investigated using random forest as the learner in detection and regression tasks. Detection evaluates the personal effectiveness of a shot with respect to the player-specific average, whereas regression estimates the value of target attribute, using EMG features as predictors. Fusion after decision optimization provided the best results: the equal error rate in detection was 24.3% for the speed and 31.7% for the distance; the mean absolute percentage error in regression was 3.2% for the speed and 6.4% for the distance. Proposed EMG feature sets were found to be useful, especially when used in combination. Rankings of feature sets indicated statistics for muscle activity in both the left and right body sides, correlation-based analysis of EMG dynamics and features derived from the properties of two highest peaks as important predictors of personal shot effectiveness. Activation sequence profiles helped in analyzing muscle orchestration during golf shot, exposing a specific avalanche pattern, but data from more players are needed for stronger conclusions. Results demonstrate that information arising from an EMG signal stream is useful for predicting golf shot success, in terms of club head speed and ball carry distance, with acceptable accuracy. Surface EMG data, collected with a goal to automatically evaluate golf player's performance, enables wearable computing in the field of ambient intelligence and has potential to enhance exercising of a long carry distance drive.

Do the acute biochemical and neuromuscular responses justify the classification of strength- and hypertrophy-type resistance exercise?

This study aimed to examine a wide profile of acute biochemical and neuromuscular responses to strength (STR) and hypertrophy (HYP) resistance exercise (RE). Seven trained men completed an STR workout (4 × 6 repetitions, 85% 1 repetition maximum [1RM], 5-minute rest periods), an HYP workout (4 × 10 repetitions, 70% 1RM, 90-second rest periods), and a control condition (CON) in a randomized crossover design. Peak force (PF), rate of force development (RFD), and muscle activity were quantified before and after exercise during an isometric squat protocol. Blood samples were taken 20, 10, and 0 minutes before and 0, 10, and 60 minutes after exercise to measure the concentration of blood lactate (BL), pH, and a number of electrolytes that were corrected for plasma volume changes. No differences were observed between the workouts for changes in PF, RFD, or muscle activity. Repeated contrasts revealed a greater (p ≤ 0.05) increase in BL concentration and reduction in pH after the HYP protocol than the STR or CON conditions. There were similar but significant (p ≤ 0.05) changes in the concentration of a number of electrolytes after both workouts, and a handful of these changes displayed significant correlations with the PF reductions observed after the HYP condition. Although the STR and HYP workouts were significantly different in terms of intensity, volume, and rest, these differences were only observable in the acid-base responses. The present findings reinforce the need for practitioners to look beyond the classification of RE workouts when aiming to elicit specific physiological responses.

Predicting Blood Lactate Concentration and Oxygen Uptake from sEMG Data during Fatiguing Cycling Exercise

This article presents a study of the relationship between electromyographic (EMG) signals from vastus lateralis, rectus femoris, biceps femoris and semitendinosus muscles, collected during fatiguing cycling exercises, and other physiological measurements, such as blood lactate concentration and oxygen consumption. In contrast to the usual practice of picking one particular characteristic of the signal, e.g., the median or mean frequency, multiple variables were used to obtain a thorough characterization of EMG signals in the spectral domain. Based on these variables, linear and non-linear (random forest) models were built to predict blood lactate concentration and oxygen consumption. The results showed that mean and median frequencies are sub-optimal choices for predicting these physiological quantities in dynamic exercises, as they did not exhibit significant changes over the course of our protocol and only weakly correlated with blood lactate concentration or oxygen uptake. Instead, the root mean square of the original signal and backward difference, as well as parameters describing the tails of the EMG power distribution were the most important variables for these models. Coefficients of determination ranging from R2=0.77 to R2=0.98 (for blood lactate) and from R2=0.81 to R2=0.97 (for oxygen uptake) were obtained when using random forest regressors.

Plasma K+ dynamics and implications during and following intense rowing exercise

We investigated whether potassium (K(+)) disturbances during and following intense exercise may be pronounced when utilizing a large contracting muscle mass, examining maximal 2,000-m rowing exercise effects on radial arterial plasma K(+) concentration ([K(+)]a) in 11 healthy adults. Blood was sampled at baseline, preexercise, each 30 s during rowing, and for 30 min postexercise. Time to complete 2,000 m was 7.26 ± 0.59 min; power output at 30 s was 326 ± 81 W (mean ± SD). With exercise time expressed in deciles, power output fell 16.5% from the first to fourth decile (P < 0.05) and 19.9% at the ninth decile (P < 0.05); EMG median frequency declined 4.6% by the third decile and 5.5% by the eighth decile (P < 0.05). Plasma [K(+)]a increased from 3.89 ± 0.13 mM at rest to 6.13 ± 0.46 mM by 90 s rowing (P < 0.001) and was then sustained until end exercise (P < 0.001). In recovery, [K(+)]a decreased abruptly, reaching 3.33 ± 0.22 mM at 5 min postexercise (P < 0.001) and remaining below preexercise after 30 min (P < 0.005). At end exercise, blood [lactate]a (preexercise 0.64 ± 0.18 mM) reached 10.87 ± 1.33 mM, plasma volume decreased 9.7 ± 2.3% from preexercise, and pHa decreased to 7.10 ± 0.07 units (P < 0.001). In conclusion, arterial hyperkalemia was sustained during exhaustive rowing reflecting a balance between K(+) release and reuptake in contracting muscles and K(+) uptake by inactive muscles. While high, the [K(+)]a was lower than anticipated compared with maximal cycling or sprinting, possibly reflecting greater adrenergic response and Na(+),K(+)-ATPase activity in contracting muscles; fatigue was evidenced by reduced power output and EMG median frequency. A prolonged hypokalemia after rowing likely reflected continuing muscular Na(+),K(+)-ATPase activity.

Effects of rehydration and food consumption on salivary flow, pH and buffering capacity in young adult volunteers during ergometer exercise

Background

The aim of this study was to investigate the influences of rehydration and food consumption on salivary flow, pH, and buffering capacity during bicycle ergometer exercise in participants.

Methods

Ten healthy volunteers exercised on a bicycle ergometer at 80% of their maximal heart rate. These sessions lasted for two periods of 20 min separated by 5-min rest intervals. Volunteers were subjected to one of the following conditions: (1) no water (mineral water) or food consumption, (2) only water for rehydration, (3) water and food consumption, (4) a sports drink only for rehydration, and (5) rehydration with a sports drink and food. Statistical significance was assessed using one-way analysis of variance and Dunnett’s test (p < 0.05).

Results

The salivary pH decreased significantly during and after exercise in conditions 4 and 5. The salivary buffering capacity decreased significantly during exercise and/or after the exercise in conditions 1, 3, 4, and 5.

Conclusions

The results showed that salivary pH and buffering capacity decreased greatly depending on the combination of a sports drink and food.

Decreased foot inversion force and increased plantar surface after maximal incremental running exercise

Formulating the hypothesis that a maximal running exercise could induce fatigue of some foot muscles, we searched for electromyographic (EMG) signs of fatigue in the tibialis anterior (TA), peroneus longus (PL), and gastrocnemius medialis (GM) muscles. We also searched for post-exercise alterations of the stationary upright standing in normal-arched feet subjects. Healthy subjects performed a maximal running exercise. Surface EMGs of the TA, PL, and GM muscles were analysed during maximal dynamic efforts. Before and after the running bout, we measured the evoked compound muscle potential (M-wave) in TA, the maximal force into inversion (MIF), and the repartition of the plantar and barycentre surfaces with a computerised stationary platform. During maximal running exercise, the median frequency of the EMG spectra declined in TA while it remained stable in the PL and GM muscles. After the exercise, MIF decreased, and both the rearfoot plantar surface and the barycentre surface increased. We concluded that a maximal running bout elicits EMG signs of fatigue, though only in the TA muscle. It also elicits post-exercise changes in the foot position during stationary upright standing which indicates a foot eversion. These data solely concern a maximal running test and they can not be extrapolated to walking or running at a low speed.