Evaluation of the Electromyography Test for the Analysis of the Aerobic-Anaerobic Transition in Elite Cyclists during Incremental Exercise

Latasa, Iban; Córdova, Alfredo; Quintana-Ortí, Gregorio; Lavilla-Oiz, Ana; Navallas, Javier; Rodriguez‐Falces, Javier

doi:10.3390/app9030589

Cited by 6 publications

(10 citation statements)

References 38 publications

(59 reference statements)

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“…Electromyography (EMG) is an electrical signal generated by skeletal muscles when they contract spontaneously [ 2 ]. EMG signal analysis can provide continuous measurement of the muscle’s state during the continuous fatigue contraction process, which is different from the subjective typical assessment that usually indicates that the subject can no longer perform the test [ 3 ]. The surface electromyography signal (sEMG) is a comprehensive effect of superficial muscle EMG and nerve trunk electrical activity on the skin surface.…”

Section: Introductionmentioning

confidence: 99%

“…Many scholars have studied muscle fatigue recognition models that can identify muscle fatigue states by detecting changes in sEMG signals. Latasa et al detected the EMG signals of cyclists in an incremental continuous cycling test and used a multi-segment linear regression algorithm to study the aerobic-anaerobic threshold transition [ 3 ]. Martinez et al recorded the subjects’ sEMG signals in the fatigue state of the vastus lateralis in the incremental cycling test and analyzed the correlation between muscle fiber conduction velocity (MFCV), instantaneous mean frequency (iMNF), normalized root mean square (RMS), and muscle fatigue state [ 10 ].…”

Section: Introductionmentioning

confidence: 99%

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A Muscle Fatigue Classification Model Based on LSTM and Improved Wavelet Packet Threshold

Wang

Sun

2021

Sensors

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Previous studies have used the anaerobic threshold (AT) to non-invasively predict muscle fatigue. This study proposes a novel method for the automatic classification of muscle fatigue based on surface electromyography (sEMG). The sEMG data were acquired from 20 participants during an incremental test on a cycle ergometer using sEMG sensors placed on the vastus rectus femoris (RF), vastus lateralis (VL), vastus medialis (VM), and gastrocnemius (GA) muscles of the left leg. The ventilation volume (VE), oxygen uptake (VO2), and carbon dioxide production (VCO2) data of each participant were collected during the test. Then, we extracted the time-domain and frequency-domain features of the sEMG signal denoised by the improved wavelet packet threshold denoising algorithm. In this study, we propose a new muscle fatigue recognition model based on the long short-term memory (LSTM) network. The LSTM network was trained to classify muscle fatigue using sEMG signal features. The results showed that the improved wavelet packet threshold function has better performance in denoising sEMG signals than hard threshold and soft threshold functions. The classification performance of the muscle fatigue recognition model proposed in this paper is better than that of CNN (convolutional neural network), SVM (support vector machine), and the classification models proposed by other scholars. The best performance of the LSTM network was achieved with 70% training, 10% validation, and 20% testing rates. Generally, the proposed model can be used to monitor muscle fatigue.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Muscle Fatigue Classification Model Based on LSTM and Improved Wavelet Packet Threshold

Wang

Sun

2021

Sensors

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“…Evidently, SI was associated with higher mechanical and metabolic stress in muscle tissue than LI, as deduced from its higher power output (14% higher than LI) and its prolonged time above 90% of VO 2max (54%). It was thus surprising that SI was not associated with increased EMG activity, as expected from previous studies 17,40 . This discrepancy may be due to numerous factors, including the higher pedaling frequency in SI compared to LI, the short breaks (15 seconds) between short work interval (30 seconds), the relative small differences in power output between SI and LI (90% vs 80% of W max ), and the small sample size in EMG measurements (n = 6 ).…”

Section: Discussionmentioning

confidence: 58%

“…It was thus surprising that SI was not associated with increased EMG activity, as expected from previous studies. 17,40 This discrepancy may be due to numerous factors, including the higher pedaling frequency in SI compared to LI, the short breaks (15 seconds) between short work interval (30 seconds), the relative small differences in power output between SI and LI (90% vs 80% of W max ), and the small sample size in EMG measurements (n = 6). In SI, the increased time above 90% of VO 2max was likely due to the combination of increased intensity during work intervals and the sustained elevation of VO 2 during 15-seconds active pauses.…”

Section: Discussionmentioning

confidence: 99%

Systemic and muscular responses to effort‐matched short intervals and long intervals in elite cyclists

Almquist

Nygaard

Vegge

et al. 2020

Scandinavian Med Sci Sports

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The purpose of this study was to compare the acute effects of time‐ and effort‐matched high‐intensity intervals on physiological, endocrine, and skeletal muscle molecular variables in elite cyclists. Eight elite cyclists performed short intervals (SI: 30‐seconds) and long intervals (LI: 5‐minutes) with work:recovery ratio 2:1, using a randomized crossover design. SI was associated with 14% ± 3% higher mean power output (SI; 421 ± 27 vs LI; 371 ± 22 W), and longer working time above 90% of maximal oxygen uptake (VO2max, 54% ± 76%) and 90% peak heart rate (HRpeak, 153% ± 148%) than LI (all P < .05), despite similar degrees of perceived exertion, blood lactate levels and muscle activation measured using EMG root mean square (EMG rms). In blood, SI was associated with more pronounced increases in testosterone and testosterone‐to‐sex hormone‐binding globulin (SHBG) ratios, as well as prolonged cortisol responses (P < .05). In skeletal muscle (m. Vastus lateralis), SI and LI led to similar changes in mRNA abundance for a range of transcripts, with the exception of NHE1 mRNA, which decreased after SI (P < .05). Overall, SI was associated with more pronounced physiological and endocrine responses than LI in elite cyclists, suggesting that such training might lead to superior adaptations in elite cyclists.

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“…During early research, authors confirmed the importance of using the EMG FT point in both recreational and professional athletes to assess exercise intensity and fatigue [ 21 ]. However, up to this point, few papers completely suggested the replacement of the means of measuring and determining the ventilatory thresholds by using the EMG method [ 22 ].…”

Section: Discussionmentioning

confidence: 99%

Using an Electromyography Method While Measuring Oxygen Uptake to Appreciate Physical Exercise Intensity in Adolescent Cyclists: An Analytical Study

Martin

Martin-Hadmaș

2021

Medicina

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Background and Objectives: During physical exercise, the electrical signal of the muscle fibers decreases due to repeated muscle contractions held at different intensities. The measured signal is strongly related to the motor unit activation rate, which is dependent on the chemical mediators and the available energy. By reducing the energy availability, adenosine triphosphate (ATP) production will decrease and therefore the muscle fibers activation rate will be negatively affected. Such aspects become important when taking into account that the training intensity for many young athletes is rather controlled by using the heart rate values. Yet, on many occasions, we have seen differences and lack of relationship between the muscle activation rate, the heart rate values and the lactate accumulation Materials and Methods: We conducted a prospective analytical study conducted during a 4-month period, on a sample of 30 participants. All study participants underwent an incremental exercise bike test to measure maximum aerobic capacity as well as the muscle activation rate in the vastus lateralis by using an electromyography method (EMG). Results: With age, the EMG signal dropped, as did the electromyography fatigue threshold (EMGFT) point, as seen through p = 0.0057, r = −0.49, CI95% = −0.73 to −0.16, and electromyography maximum reached point (EMGMRP) (p = 0.0001, r = −0.64, CI95% = −0.82 to −0.36), whereas power output increased (p = 0.0186, r = 0.427). The higher the power output, the lower the signal seen by measuring active tissue EMGFT (p = 0.0324, r = −0.39) and EMGMRP (p = 0.0272, r = −0.40). Yet, with changes in median power output, the power developed in aerobic (p = 0.0087, r = 0.47), mixed (p = 0.0288, r = 0.39), anaerobic (p = 0.0052, r = 0.49) and anaerobic power (p = 0.004, r = 0.50) exercise zones increased. Conclusions: There has been reported a relationship between aerobic/anaerobic ventilatory thresholds (VT1 and VT2) and EMGFT, EMGMRP, respectively. Each change in oxygen uptake increased the power output in EMGFT and EMGMRP, improving performances and therefore overlapping with both ventilatory thresholds.

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Evaluation of the Electromyography Test for the Analysis of the Aerobic-Anaerobic Transition in Elite Cyclists during Incremental Exercise

Cited by 6 publications

References 38 publications

A Muscle Fatigue Classification Model Based on LSTM and Improved Wavelet Packet Threshold

A Muscle Fatigue Classification Model Based on LSTM and Improved Wavelet Packet Threshold

Systemic and muscular responses to effort‐matched short intervals and long intervals in elite cyclists

Using an Electromyography Method While Measuring Oxygen Uptake to Appreciate Physical Exercise Intensity in Adolescent Cyclists: An Analytical Study

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