European Journal of Applied Physiology, Jun 1, 1996
Eight subjects performed a single allout sprint on a cycle ergometer with strain gauges bonded to... more Eight subjects performed a single allout sprint on a cycle ergometer with strain gauges bonded to the cranks. The crank angle-torque curves of the left and right legs were recorded during ten revolutions using the software package supplied with the ergometer. Torque data were stored every 2 degrees (180 angletorque data per pedal revolution for each leg). The ergometer was used in the linear mode with the lowest available linear factor (F1 = 0.01). In this mode, the braking torque (TB) was proportional to cycling velocity v(TB = F1v) and mechanical power was equal to F1v2. The relationship between the torque averaged over one revolution and the average velocity of one pedal revolution was studied during the acceleration phase of short allout exercise on an electronic ergometer (eight subjects) and a friction-loaded ergometer (four subjects). The present study showed that it is possible to determine the maximal torque-velocity relationship and to calculate maximal anaerobic power during a single allout sprint using an electronic cycle ergometer provided that strain gauges are bonded to the cranks. The torque-velocity relationships calculated were linear as for a friction loaded ergometer. As expected, the values of torque and maximal power measured with the strain gauges were higher than the corresponding values computed from the data collected during an allout test on a friction loaded ergometer. The torque-angle data collected during a single allout cycling exercise would suggest that angular accelerations of the leg segments and gravitational forces play the main role at high velocity.
Cet article propose une approche neuroergonomique et sa mise en œuvre visant la caracterisation d... more Cet article propose une approche neuroergonomique et sa mise en œuvre visant la caracterisation de la motricite en situation quotidienne, chez des personnes souffrant de troubles neurologiques. Sont plus particulierement etudiees, dans le cas specifique de la maladie de Parkinson, les activites de marche considerees ici comme des activites complexes et situees. Nous proposons, dans ce cadre, une methodologie d’evaluation ecologique integrant observations ethnographiques, recueil de l’experience subjective et enregistrements physiologiques et biomecaniques (electromyographie, accelerometrie,..) deployee dans le cas d’une femme de 54 ans, dont la maladie est diagnostiquee depuis 10 ans. Les donnees permettent 1) d’identifier des phases motrices significatives relatives a l’experience corporelle vecue dans la dynamique des prises medicamenteuses, 2) d’enrichir des categories de configurations de marche emergeant dans un couplage individu/environnement, 3) de caracteriser des situations de vulnerabilites specifiques. Nous discutons ces resultats et le potentiel de cette approche neuroergonomique au regard de la coherence et de la complementarite des differents niveaux de donnees, des applications cliniques, en lien avec les enjeux actuels de l’education therapeutique, de la formation des professionnels de sante et de la medecine personnalisee.
Ordinal pattern-based approaches have great potential to capture intrinsic structures of dynamica... more Ordinal pattern-based approaches have great potential to capture intrinsic structures of dynamical systems, and therefore, they continue to be developed in various research fields. Among these, the permutation entropy (PE), defined as the Shannon entropy of ordinal probabilities, is an attractive time series complexity measure. Several multiscale variants (MPE) have been proposed in order to bring out hidden structures at different time scales. Multiscaling is achieved by combining linear or nonlinear preprocessing with PE calculation. However, the impact of such a preprocessing on the PE values is not fully characterized. In a previous study, we have theoretically decoupled the contribution of specific signal models to the PE values from that induced by the inner correlations of linear preprocessing filters. A variety of linear filters such as the autoregressive moving average (ARMA), Butterworth, and Chebyshev were tested. The current work is an extension to nonlinear preprocessin...
Surface electromyography (sEMG) is a valuable technique that helps provide functional and structu... more Surface electromyography (sEMG) is a valuable technique that helps provide functional and structural information about the electric activity of muscles. As sEMG measures output of complex living systems characterized by multiscale and nonlinear behaviors, Multiscale Permutation Entropy (MPE) is a suitable tool for capturing useful information from the ordinal patterns of sEMG time series. In a previous work, a theoretical comparison in terms of bias and variance of two MPE variants—namely, the refined composite MPE (rcMPE) and the refined composite downsampling (rcDPE), was addressed. In the current paper, we assess the superiority of rcDPE over MPE and rcMPE, when applied to real sEMG signals. Moreover, we demonstrate the capacity of rcDPE in quantifying fatigue levels by using sEMG data recorded during a fatiguing exercise. The processing of four consecutive temporal segments, during biceps brachii exercise maintained at 70% of maximal voluntary contraction until exhaustion, shows...
Permutation Entropy (PE) is a powerful tool for measuring the amount of information contained wit... more Permutation Entropy (PE) is a powerful tool for measuring the amount of information contained within a time series. However, this technique is rarely applied directly on raw signals. Instead, a preprocessing step, such as linear filtering, is applied in order to remove noise or to isolate specific frequency bands. In the current work, we aimed at outlining the effect of linear filter preprocessing in the final PE values. By means of the Wiener–Khinchin theorem, we theoretically characterize the linear filter’s intrinsic PE and separated its contribution from the signal’s ordinal information. We tested these results by means of simulated signals, subject to a variety of linear filters such as the moving average, Butterworth, and Chebyshev type I. The PE results from simulations closely resembled our predicted results for all tested filters, which validated our theoretical propositions. More importantly, when we applied linear filters to signals with inner correlations, we were able t...
2019 27th European Signal Processing Conference (EUSIPCO), 2019
Multiscale Permutation Entropy (MPE), an extension of Permutation Entropy (PE), was proposed to b... more Multiscale Permutation Entropy (MPE), an extension of Permutation Entropy (PE), was proposed to better capture the information content in long range trends. This technique has been extensively used in biomedical applications for diagnosis purposes. Although PE theory is well established and explored, there is still a lack of theoretical development for MPE. In the present paper, we expand the theory by formulating an explicit MPE model of first order Autoregressive (AR) and Moving Average (MA) processes, which are well known and used in signal modeling. We first build the autocorrelation function of coarse-grained AR and MA models, which are a prerequisite for MPE calculation. Next, we use the resulting autocorrelation functions to establish the theoretical value of MPE as a function of time scale and AR or MA parameters. The theoretical result is tested against MPE measurements from simulations. We found the MPE of the 1o order AR model to converge to the maximum entropy with increasing time scale. Nonetheless, the convergence is not always monotonic. For AR parameter values greater than the Golden Ratio, the MPE curve presents a local minimum at a time scale different than one, which implies a more regular structure than the one measured with PE. The MPE of the 1o order MA model converges rapidly to the maximum entropy with increasing time scales, regardless of the MA parameter value, which is in accordance to our expectations.
To increase the diagnostic accuracy, artificial intelligence techniques can be used as a medical ... more To increase the diagnostic accuracy, artificial intelligence techniques can be used as a medical support. The Electromyography (EMG) signals are used in the neuromuscular dysfunction evaluation. The aim of this paper is to construct an automatic system of neuromuscular dysfunction identification in the case of the Parkinson disease based on surface EMG (sEMG) signals. Our proposed system uses artificial neural network method (ANN) to discriminate healthy EMG signals (normal) from abnormal EMG signals (Parkinson). After detecting the EMG activity regions using Fine Modified Adaptive Linear Energy Detecor (FM-ALED) method, Discrete Wavelet Transform (DWT) has been used for feature extraction. An experimental analysis is carried out using ECOTECH’s project dataset using principally the Accuracy (Acc). Moreover, a multi-class neural networks classification system combined with the voting rule and Wavelet Cepstral Coefficient (WCC) for healthy and Parkinsonian subjects identification has...
Background The Multiscale Permutation Entropy (MPE) is a powerful tool in the differentiation of ... more Background The Multiscale Permutation Entropy (MPE) is a powerful tool in the differentiation of physiological electrical activity. In particular, the literature has found a clear link between the presence of faults in rotatory machines signals (Zheng 2018), and a reduction in Entropy within them. Therefore, any improvement in the precision of the MPE estimation enhances the chances of detecting increasingly nuanced changes in fault detection. Objectives In the present work, we first provide an alternative Permutation Entropy approach: the Refined Composite Downsampling Multiscale Permutation Entropy (rcDPE), which further reduces the variance over Refined Composite Multiscale Permutation Entropy (rcMPE) [Humeau-Heutier, 2015], by applying an alternative to the widely used coarse-graining procedure for multiscaling. Methodology Using the Bechhoffer bearing fault dataset (2013), we performed a 3-way ANOVA test with the following factors: Type of signal (presence of faults), Method, a...
European Journal of Applied Physiology, Jun 1, 1996
Eight subjects performed a single allout sprint on a cycle ergometer with strain gauges bonded to... more Eight subjects performed a single allout sprint on a cycle ergometer with strain gauges bonded to the cranks. The crank angle-torque curves of the left and right legs were recorded during ten revolutions using the software package supplied with the ergometer. Torque data were stored every 2 degrees (180 angletorque data per pedal revolution for each leg). The ergometer was used in the linear mode with the lowest available linear factor (F1 = 0.01). In this mode, the braking torque (TB) was proportional to cycling velocity v(TB = F1v) and mechanical power was equal to F1v2. The relationship between the torque averaged over one revolution and the average velocity of one pedal revolution was studied during the acceleration phase of short allout exercise on an electronic ergometer (eight subjects) and a friction-loaded ergometer (four subjects). The present study showed that it is possible to determine the maximal torque-velocity relationship and to calculate maximal anaerobic power during a single allout sprint using an electronic cycle ergometer provided that strain gauges are bonded to the cranks. The torque-velocity relationships calculated were linear as for a friction loaded ergometer. As expected, the values of torque and maximal power measured with the strain gauges were higher than the corresponding values computed from the data collected during an allout test on a friction loaded ergometer. The torque-angle data collected during a single allout cycling exercise would suggest that angular accelerations of the leg segments and gravitational forces play the main role at high velocity.
Cet article propose une approche neuroergonomique et sa mise en œuvre visant la caracterisation d... more Cet article propose une approche neuroergonomique et sa mise en œuvre visant la caracterisation de la motricite en situation quotidienne, chez des personnes souffrant de troubles neurologiques. Sont plus particulierement etudiees, dans le cas specifique de la maladie de Parkinson, les activites de marche considerees ici comme des activites complexes et situees. Nous proposons, dans ce cadre, une methodologie d’evaluation ecologique integrant observations ethnographiques, recueil de l’experience subjective et enregistrements physiologiques et biomecaniques (electromyographie, accelerometrie,..) deployee dans le cas d’une femme de 54 ans, dont la maladie est diagnostiquee depuis 10 ans. Les donnees permettent 1) d’identifier des phases motrices significatives relatives a l’experience corporelle vecue dans la dynamique des prises medicamenteuses, 2) d’enrichir des categories de configurations de marche emergeant dans un couplage individu/environnement, 3) de caracteriser des situations de vulnerabilites specifiques. Nous discutons ces resultats et le potentiel de cette approche neuroergonomique au regard de la coherence et de la complementarite des differents niveaux de donnees, des applications cliniques, en lien avec les enjeux actuels de l’education therapeutique, de la formation des professionnels de sante et de la medecine personnalisee.
Ordinal pattern-based approaches have great potential to capture intrinsic structures of dynamica... more Ordinal pattern-based approaches have great potential to capture intrinsic structures of dynamical systems, and therefore, they continue to be developed in various research fields. Among these, the permutation entropy (PE), defined as the Shannon entropy of ordinal probabilities, is an attractive time series complexity measure. Several multiscale variants (MPE) have been proposed in order to bring out hidden structures at different time scales. Multiscaling is achieved by combining linear or nonlinear preprocessing with PE calculation. However, the impact of such a preprocessing on the PE values is not fully characterized. In a previous study, we have theoretically decoupled the contribution of specific signal models to the PE values from that induced by the inner correlations of linear preprocessing filters. A variety of linear filters such as the autoregressive moving average (ARMA), Butterworth, and Chebyshev were tested. The current work is an extension to nonlinear preprocessin...
Surface electromyography (sEMG) is a valuable technique that helps provide functional and structu... more Surface electromyography (sEMG) is a valuable technique that helps provide functional and structural information about the electric activity of muscles. As sEMG measures output of complex living systems characterized by multiscale and nonlinear behaviors, Multiscale Permutation Entropy (MPE) is a suitable tool for capturing useful information from the ordinal patterns of sEMG time series. In a previous work, a theoretical comparison in terms of bias and variance of two MPE variants—namely, the refined composite MPE (rcMPE) and the refined composite downsampling (rcDPE), was addressed. In the current paper, we assess the superiority of rcDPE over MPE and rcMPE, when applied to real sEMG signals. Moreover, we demonstrate the capacity of rcDPE in quantifying fatigue levels by using sEMG data recorded during a fatiguing exercise. The processing of four consecutive temporal segments, during biceps brachii exercise maintained at 70% of maximal voluntary contraction until exhaustion, shows...
Permutation Entropy (PE) is a powerful tool for measuring the amount of information contained wit... more Permutation Entropy (PE) is a powerful tool for measuring the amount of information contained within a time series. However, this technique is rarely applied directly on raw signals. Instead, a preprocessing step, such as linear filtering, is applied in order to remove noise or to isolate specific frequency bands. In the current work, we aimed at outlining the effect of linear filter preprocessing in the final PE values. By means of the Wiener–Khinchin theorem, we theoretically characterize the linear filter’s intrinsic PE and separated its contribution from the signal’s ordinal information. We tested these results by means of simulated signals, subject to a variety of linear filters such as the moving average, Butterworth, and Chebyshev type I. The PE results from simulations closely resembled our predicted results for all tested filters, which validated our theoretical propositions. More importantly, when we applied linear filters to signals with inner correlations, we were able t...
2019 27th European Signal Processing Conference (EUSIPCO), 2019
Multiscale Permutation Entropy (MPE), an extension of Permutation Entropy (PE), was proposed to b... more Multiscale Permutation Entropy (MPE), an extension of Permutation Entropy (PE), was proposed to better capture the information content in long range trends. This technique has been extensively used in biomedical applications for diagnosis purposes. Although PE theory is well established and explored, there is still a lack of theoretical development for MPE. In the present paper, we expand the theory by formulating an explicit MPE model of first order Autoregressive (AR) and Moving Average (MA) processes, which are well known and used in signal modeling. We first build the autocorrelation function of coarse-grained AR and MA models, which are a prerequisite for MPE calculation. Next, we use the resulting autocorrelation functions to establish the theoretical value of MPE as a function of time scale and AR or MA parameters. The theoretical result is tested against MPE measurements from simulations. We found the MPE of the 1o order AR model to converge to the maximum entropy with increasing time scale. Nonetheless, the convergence is not always monotonic. For AR parameter values greater than the Golden Ratio, the MPE curve presents a local minimum at a time scale different than one, which implies a more regular structure than the one measured with PE. The MPE of the 1o order MA model converges rapidly to the maximum entropy with increasing time scales, regardless of the MA parameter value, which is in accordance to our expectations.
To increase the diagnostic accuracy, artificial intelligence techniques can be used as a medical ... more To increase the diagnostic accuracy, artificial intelligence techniques can be used as a medical support. The Electromyography (EMG) signals are used in the neuromuscular dysfunction evaluation. The aim of this paper is to construct an automatic system of neuromuscular dysfunction identification in the case of the Parkinson disease based on surface EMG (sEMG) signals. Our proposed system uses artificial neural network method (ANN) to discriminate healthy EMG signals (normal) from abnormal EMG signals (Parkinson). After detecting the EMG activity regions using Fine Modified Adaptive Linear Energy Detecor (FM-ALED) method, Discrete Wavelet Transform (DWT) has been used for feature extraction. An experimental analysis is carried out using ECOTECH’s project dataset using principally the Accuracy (Acc). Moreover, a multi-class neural networks classification system combined with the voting rule and Wavelet Cepstral Coefficient (WCC) for healthy and Parkinsonian subjects identification has...
Background The Multiscale Permutation Entropy (MPE) is a powerful tool in the differentiation of ... more Background The Multiscale Permutation Entropy (MPE) is a powerful tool in the differentiation of physiological electrical activity. In particular, the literature has found a clear link between the presence of faults in rotatory machines signals (Zheng 2018), and a reduction in Entropy within them. Therefore, any improvement in the precision of the MPE estimation enhances the chances of detecting increasingly nuanced changes in fault detection. Objectives In the present work, we first provide an alternative Permutation Entropy approach: the Refined Composite Downsampling Multiscale Permutation Entropy (rcDPE), which further reduces the variance over Refined Composite Multiscale Permutation Entropy (rcMPE) [Humeau-Heutier, 2015], by applying an alternative to the widely used coarse-graining procedure for multiscaling. Methodology Using the Bechhoffer bearing fault dataset (2013), we performed a 3-way ANOVA test with the following factors: Type of signal (presence of faults), Method, a...
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