Olivier Buttelli

Effect of fatigue on maximal velocity and maximal torque during short exhausting cycling

A group of 24 subjects performed on a cycle ergometer a fatigue test consisting of four successive all-out sprints against the same braking torque. The subjects were not allowed time to recover between sprints and consequently the test duration was shorter than 30 s. The pedal velocity was recorded every 10 ms from a disc fixed to the flywheel with 360 slots passing in front of a photo-electric cell linked to a microcomputer which processed the data. Taking into account the variation of kinetic energy of the ergometer flywheel, it was possible to determine the linear torque-velocity relationship from data obtained during the all-out cycling exercise by computing torque and velocity from zero velocity to peak velocity according to a method proposed previously. The maximal theoretical velocity (ν1) and the maximal theoretical torque (T1) were estimated by extrapolation of each torque-velocity relationship. Maximal power (Pmax) was calculated from the values of T0 and ν0 (Pmax = 0.25ν0T0). The kinetics of ν0, T0 and Pmax was assumed to express the effects of fatigue on the muscle contractile properties (maximal shortening velocity, maximal muscle strength and maximal power). Fatigue induced a parallel shift to the left of the torque-velocity relationships. The ν0, T0 and Pmax decreases were equal to 16.3%, 17.3% and 31%, respectively. The magnitude of the decrease was similar for ν0 and T0 which suggested that Pmax decreased because of a slowing of maximal shortening velocity as well as a loss in maximal muscle strength. However, the interpretation of a decrease in cycling ν0 which has the dimension of a maximal cycling frequency is made difficult by the possible interactions between the agonistic and the antagonistic muscles and could also be explained by a slowing of the muscle relaxation rate.

The vastus lateralis neuromuscular activity during all-out cycling exercise.

OBJECTIVE The objective of this work was to study modifications in motor control through surface electromyographic (sEMG) activity during a very short all-out cycling exercise. METHODS Twelve male cyclists (age 23+/-4 years) participated in this study. After a warm-up period, each subject performed three all-out cycling exercises of 6s separated by 2 min of complete rest. This protocol was repeated three times with a minimum of 2 days between each session. The braking torque imposed on cycling motion was 19 Nm. The sEMG of the vastus lateralis was recorded during the first seven contractions of the sprint. Time-frequency analysis of sEMG was performed using continuous wavelet transform. The mean power frequency (MPF, qualitative modifications in the recruitment of motor units) and signal energy (a quantitative indicator of modifications in the motor units recruitment) were computed for the frequency range 10-500 Hz. RESULTS sEMG energy increased (P0.05) between contraction number 1 and 2, decreased (P < or =0.05) between contraction number 2 and 3 then stabilized between contraction number 3 and 7 during the all-out test. MPF increased (P < or =0.05) during the all-out test. This increase was more marked during the first two contractions. CONCLUSIONS The decrease in energy and the increase in the sEMG MPF suggest a large spatial recruitment of motor units (MUs) at the beginning of the sprint followed by a preferential recruitment of faster MUs at the end of the sprint, respectively.

Effects of aerobic exercise on the torque-velocity relationship in cycling

Abstract The kinetics of the torque-velocity (T-ω) relationship after aerobic exercise was studied to assess the effect of fatigue on the contractile properties of muscle. A group of 13 subjects exercised until fatigued on a cycle ergometer, at an intensity which corresponded to 60% of their maximal aerobic power for 50 min (MAP60%); ten subjects exercised until fatigued at 80% of their maximal aerobic power for 15 min (MAP80%). Of the subjects 7 exercised at both intensities with at least a 1-week interval between sessions. Pedalling rate was set at 60 rpm. The T-ω relationship was determined from the velocity data collected during all-out sprints against a 19 N · m braking torque on the same ergometer, according to a method proposed previously. Maximal theoretical velocity (ω0) and maximal theoretical torque (T0) were estimated by extrapolation of the linear T-ω relationship. Maximal power (Pmax) was calculated from the values of T0 and ω0 (Pmax = 0.25 ω0 T0). The T-ω relationships were determined before, immediately after and 5 and 10 min after the aerobic exercise. The kinetics of ω0, T0 and Pmax was assumed to express the effects of fatigue on the muscle contractile properties (maximal shortening velocity, maximal muscle strength and maximal power). Immediately after exercise at MAP60% a 7.8% decrease in T0 and 8.8% decrease in Pmax was seen while the decrease in ω0 was nonsignificant, which suggested that Pmax decreased in the main because of a loss in maximal muscle strength. In contrast, MAP80% induced a 8.1% decrease in ω0 and 12.8% decrease in Pmax while the decrease in T0 was nonsignificant, which suggested that the main cause of the decrease in Pmax was probably a slowing of maximal shortening velocity. The short recovery time of the T-ω relationship suggests that the causes of the decrease of torque and velocity are processes which recover rapidly.

Recovery of the torque-velocity relationship after short exhausting cycling exercise

Abstract The effects of fatigue upon the torque-velocity (T-ω) relationship in cycling were studied in 11 subjects. Fatigue was induced by short exhausting exercise, on a cycle ergometer, consisting of 4 all-out sprints without recovery. The linear (T-ω) relationship was determined during each all-out sprint, before, during and after the exhausting exercise. The kinetics of the T-ω relationship had permitted the study of the recovery of optimal torque, optimal velocity and their corresponding maximal power outputs (Pmax), 30 s or 1 min after the short exhausting exercise. Fatigue induced a parallel shift to the left of the T-ω relationship which was partly reversed by a parallel shift to the right during recovery. After 30 s recovery optimal velocity, optimal torque and Pmax were slightly lower than the corresponding values before the exhausting exercise; after 1-min optimal velocity and optimal torque had recovered 99% and 97% of their initial values. These mechanical data suggested that the causes of exhaustion were processes that allowed fast recovery of both optimal velocity and optimal torque.

Time-varying delay estimators for measuring muscle fiber conduction velocity from the surface electromyogram

Abstract Muscle fiber conduction velocity (MFCV) can be measured by estimating the time delay between surface EMG signals recorded by electrodes aligned with the fiber direction. In the case of dynamic contractions, the EMG signal is highly non-stationary and the time delay between recording sites may vary rapidly over time. Thus, the processing methods usually applied in the case of static contractions do not hold anymore and the delay estimation requires processing techniques that are adapted to non-stationary conditions. The current paper investigates several methods based on time-frequency approaches or adaptive filtering in order to solve the time-varying delay estimation problem. These approaches are theoretically analyzed and compared by Monte–Carlo simulations in order to determine if their performance is sufficient for practical applications. Moreover, results obtained on experimental signals recorded during cycling from the vastus medialis muscle are also shown. The study presents for the first time a set of approaches for instantaneous delay estimation from two-channels EMG signals.

A generating model of realistic synthetic heart sounds for performance assessment of phonocardiogram processing algorithms

Abstract A new model which is capable of generating realistic synthetic phonocardiogram (PCG) signals is introduced based on three coupled ordinary differential equations. The new PCG model takes into account the respiratory frequency, the heart rate variability and the time splitting of first and second heart sounds. This time splitting occurs with each cardiac cycle and varies with inhalation and exhalation. Clinical PCG statistics and the close temporal relationship between events in ECG and PCG are used to deduce values of PCG model parameters. In comparison with published PCG models, the proposed model allows a larger number of known PCG features to be taken into consideration. Moreover it is able to generate both normal and abnormal realistic synthetic heart sounds. Results show that these synthetic PCG signals have the closest features to those of a conventional heart sound in both time and frequency domains. Additionally, a sound quality test carried out by eight cardiologists demonstrates that the proposed model outperforms the existing models. This new PCG model is promising and useful in assessing signal processing techniques which are developed to help clinical diagnosis based on PCG.

Do the generalized correlation methods improve time delay estimation of the muscle fiber conduction velocity

Muscle fiber conduction velocity is generally measured by the estimation of the time delay between electromyography recording channels. In the present paper, we propose to identify the best estimator of a constant time delay among those based on generalized correlation methods. To this end, small observation windows are considered and the fractional part of time delay was calculated using a parabolic interpolation. The results show that only Eckart and Hannan-Thomson approaches outperform the basic cross-correlation method when the signal to noise ratio (SNR) is up to 0 dB and the observation duration is 250 ms. This study will be a background for further extension to time-varying delay estimation.

A new cyclostationarity-based blind approach for motor unit's firing rate automated detection in electromyographic signals

This work focuses on electromyographic (EMG) signal processing. We propose a new blind approach that aims at detecting the firing rates of the activated motor units. The proposed method is based on the fact that, EMGs can be modelled as second-order cyclostationary signals. After application of a Blind Source Separation (BSS) algorithm, we compute a cyclostationarity measure which is the Cyclic Spectral Density (CSD), and we show how one can use it to group the estimated components into independent subspaces and in an automated manner. The proposed classification procedure is based on the concept of subspace BSS techniques, like the Multidimensional Independent Component Analysis (MICA), the difference being that our method allows automatic classification of the estimated source signals. After discarding the subspace corresponding to the noise and computation of a modified CSD measure, the proposed procedure yields to the detection of specific cyclic frequencies corresponding to the discharge frequencies of the Motor Units Action Potential Trains (MUAPTs). Early results obtained from experiments on synthetic EMGs are presented in the paper and research perspectives conclude this work.

Cramer-Rao lower bounds for estimating the time-varying delay of surface EMG signals

The muscle fiber conduction velocity (CV) is usually used as a muscle fatigue indicator. The CV evaluation can indirectly be performed by estimating the time delay between surface electromyography (sEMG) signals recorded on electrodes aligned with the muscle fiber direction. To take into account the variability of the CV along the fiber and between channel recordings, the recently published methods assume that the time delay between the channels is a function depending on time. In the present paper, we derive the theoretical Cramer-Rao Bound (CRB) appropriate for estimating the time-varying delay of sEMG signals. The new CRB expression is computed for a polynomial model of the time-varying delay and for two channels. We emphasize the relationship between this new CRB expression and the classical CRB calculated for a constant time delay. Monte Carlo simulations are conducted to assess the performance of the maximum likelihood estimator of the time-varying delay. The likelihood maximization is achieved by using a stochastic optimization technique called the simulated annealing. The simulation results show the CRB derived very optimistic.

Detection of sEMG muscle activation intervals using Gaussian Mixture Model and Ant Colony Classifier

A new efficient and user-independent technique for the detection of muscle activation (MA) intervals is proposed based on Gaussian Mixture Model (GMM) and Ant Colony Classifier (AntCC). First, time and frequency features are extracted from the surface electromyography (sEMG) signals. Then, GMM is used to cluster these extracted features into burst & non burst. Those features with their class name are then used as the input for the AntCC algorithm in order to derive classification rules. Finally, the obtained rules are used to detect sEMG activation timing of human skeletal muscles during movement. The performance of the proposed technique is demonstrated by means of synthetic simulated sEMG signals and real ones. The proposed technique is then compared to two previously published techniques: wavelet transform-based method [1] & double threshold-based method [2]. It is concluded that our technique outperforms those methods and significantly improves the accuracy of good MA timing detection. Moreover, to our knowledge, the proposed technique is the first user-independent one since no tuning parameters are required. Our findings show that the proposed method is convenient for automatically processing large amounts of sEMG signals with performance beyond that of the state of the-art methods.