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EMG frequency during sustained submaximal isometric activity of biceps brachii: a linear model
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Journal of Biomechanics 2006, Vol. 39 (Suppl 1)
Oral Presentations
7503 Fr, 11:30-11:45 (P50) EMG frequency during sustained submaximal isometric activity of biceps brachii: a linear model S. Solnik 1, S. Arya 2, K. Grzegorczyk 3, A. Koziatek 1, T. Bober 1. 1University
7690 Fr, 12:00-12:15 (P50) Sampling rate dependence of amplitude-frequency correlation in EMG signals A. Siemier~ski 1, A. Kebel 1, P. Klajner 2. 1Biomechanics Laboratory, University
School of Physical Education, Wroclaw, Poland, 2University of Southern California, Los Angeles, USA, 3University of Technology, Wroclaw, Poland
School of Physical Education, Wroctaw, Poland, 2Faculty of Fundamental Problems of Technology, Wroctaw University of Technology, Wroctaw, Poland
Introduction: Neuromuscular fatigue is an important factor influencing performance. Frequency characteristics of electromyographic (EMG) signals reflect the underlying physiological processes related with muscle fatigue. We hypothesized that these physiological processes remain relatively constant between individuals. Thus, the aim of this study was to develop a mathematical model to characterize EMG signal frequency changes during a fatiguing isometric task. Methods: Twenty four healthy male subjects were recruited for this study. Surface EMG (Ag-AgCI, 1000Hz) of biceps brachii and force (isokinetic dynamometer, 100 Hz) data were recorded while the subjects performed a submaximal (80% of maximal voluntary contraction) isometric contraction of elbow flexors for 15 seconds. Zero-Crossing-Rate (ZCR) in every 1-second period was calculated to identify shifts in EMG frequency. Multiple linear regression was used to build a mathematical representation of frequency changes. Results: 1st order linear regression equation provided a good representation of EMG frequency shifts (r2 min = 0.47, r2 max = 0.93). Pearson's correlation revealed a statistically significant (r = -0.72, p < 0.05) relation between regression coefficients. An adjusted mathematical model, based on relationship between the regression coefficients, 151 and 150, was formulated to characterize EMG frequency. The maximum difference between calculated and observed values of ZCR for all subjects was 7.2%. Discussion: The mathematical model developed in this study provided an accurate description of frequency changes during submaximal isometric muscle contractions to fatigue. The observed relation between regression coefficients allowed modification of the equation to characterize subject-specific frequency changes by substituting initial frequency values specific to each individual. The results of this study may help to objectively assess level of fatigue during task performance in athletic and patient populations.
Introduction: The extent or onset of fatigue is typically assessed by simultaneous monitoring of frequency content and amplitude of surface EMG signals collected from engaged muscles. When activity level is kept unchanged with time, fatigue results in a downward shift of the frequency spectrum of the signal and an increase of signal amplitude. However, such negative amplitudefrequency correlations occur also at no fatigue due to varying signal to noise ratio, which could blur the interpretation of EMG signal behavior in terms of fatigue. Objective: To assess the magnitude of this effect and its dependence on the sampling rate of EMG signals. Material and Method: Nineteen healthy male subjects volunteered for the study. Surface EMG electrodes were used to record the activity of the vastus lateralis muscle during two-second-long trials of isometric activity corresponding to knee extending moments of 5%, 10%, 20%, 30% and 50% of MVC. The sequence of activity level was random to avoid the effects of fatigue. The EMG signals were originally sampled at a rate of 5 kHz and then processed off-line to simulate lower sampling rates ranging between 50 Hz and the original sampling rate. Two measures of amplitude (RMS and MAV (mean absolute value)) and two measures of dominating frequency (Fmed, Fmean) of the EMG signals were computed for each trial and for each simulated sampling rate. Results and Conclusions: Negative nonlinear Spearman and Kendall correlation coefficients between Fmed and RMS (MAV), as well as between Fmean and RMS (MAV) were found. The absolute value of these coefficients increased when the sampling rate increased, and the effect was especially noticeable for Fmean, even at sampling rates as low as 1 kHz. It is concluded that this correlation, which was found here at no fatigue, may be behind a part of the downward frequency shift normally attributed to fatigue. To minimize the effect, oversampling of EMG signals should be avoided.
7462 Fr, 12:15-12:30 (P51) The interplay of sensorimotor time-delays and noise in multisensory integration M. Venkadesan 1, M. Srinivasan 2, J. Guckenheimer 3, EJ. Valero-Cuevas 1.
7594 Fr, 14:00-14:15 (P52) An emg-driven model to investigate cocontraction of lower extremity muscles during gait
1Sibley School of Mechanical & Aerospace Engineering, Ithaca, NY, USA, 2 Theoretical and Applied Mechanics, Ithaca, NY, USA, 3Department of Mathematics, Cornell University, Ithaca, NY, USA
University of South Carolina, Charleston, USA, 2Mechanical Engineering, The Ohio State University, Columbus, USA, 3Industrial Engineering, North Carolina State University, Raleigh, USA
The dominant thinking in sensorimotor control is that minimizing effects of sensorimotor noise explains multisensory integration, referred to as the minimumvariance principle [1]. Contrarily, when we perform a sensorimotor task, such as handling an object, timely as well as accurate control is imperative so that we do not drop it. Hence, we hypothesized that multisensory integration should emerge from interplay of sensorimotor time-delays and noise. We tested our hypothesis by altering available sensory modalities (thumbpad sensation/vision) during a novel task of object manipulation at the boundary of instability, thus enabling us to model the system using bifurcation theory [2] that characterizes dynamics at a transition to instability. We asked 12 consenting subjects to maximally compress a slender spring using only their thumbpad without letting it slip. We modeled spring buckling dynamics as a subcritical pitchfork bifurcation [2] and sensorimotor control as multisensory proportional feedback with time-delays and noise. Both experimentally and computationally, the loss of thumbpad sensation significantly affected performance since this modality is both accurate and fast. Experimentally, lack of vision degraded performance only when thumbpad sensation was absent. Our model found that sensory weights emerging from a trade-off between time-delays and noise yielded better performance than the minimum-variance principle. Additionally an extra time-delay for non-digital mechanoreceptors over and above sensory and nerve-conduction delays was necessary to explain why lack of vision affected performance only when thumbpad sensation was absent. This additional delay is only due to computational time-delays. To our knowledge, our results show for the first time that, (i) neural computational time-delays can measurably affect performance and (ii) multisensory integration emerges from a trade-off between time-delays and noise. Work supported by NSF 0237258, NIH R21-HD048566 grants to FVC.
The purpose of this study was to introduce an EMG-driven neuromusculoskeletal model to predict cocontraction among individual musculotendon units that comprise the synergistic and antagonistic muscle groups involved in knee flexion/extension during normal walking gait. Description of the muscle's mechanical response was based on Hill's and Zajac's work, but incorporated individual muscle length, velocity, and excitation considerations. Processed EMG represented the muscle's neural input. A musculoskeletal model defining joint kinematics, muscle line of action and architecture of the left lower limb was developed by using SIMM [1] and modifying its associated model. Muscle kinematics were then calculated in conjunction with three-dimensional motion data. Computer simulations combined with a novel calibration procedure which simulated the knee-hip and knee-ankle interactions during gait allowed the estimation of all model parameters. Muscle force profiles were predicted for selected muscles of the lower limb using four subjects during normal walking. Model validation was performed against inverse dynamics net joint moments. A new validation approach was performed through estimated muscle gains, which in our study were within physiological range, and a good match was obtained between moment curves. Correlations ranged between 0.73~).97 for the gait trials and RMS differences between 22.2-12.99 Nm. The results were similar or better than those previously reported. Cocontraction indexes were higher during events in the gait cycle that required stability and control at the joint. This model provides a solid foundation for further improvements that will be discussed. The results support the feasibility of using the proposed model, as a potential solution to the interdeterminancy problem providing solutions to muscle forces involved in normal human movement, and a cocontraction index to assess muscle balance during gait.
References [1] Ernst M.O., Banks M.S. Nature 2002; 415: 429-433. [2] Guckenheimer J., Holmes P. 2002; Springer, New York.
References [1] S. Delp, P. Loan. A graphics-based software system to develop and analyze models of musculoskeletal structures. Comput. Biol. Med. 1995; 25: 21-34.
T. Karakostas 1, N. Berme 2, S. Hsiang 3 . 1Motion Analysis Laboratory, Medical
Journal of Biomechanics 2006, Vol. 39 (Suppl 1)
Oral Presentations
7503 Fr, 11:30-11:45 (P50) EMG frequency during sustained submaximal isometric activity of biceps brachii: a linear model S. Solnik 1, S. Arya 2, K. Grzegorczyk 3, A. Koziatek 1, T. Bober 1. 1University
7690 Fr, 12:00-12:15 (P50) Sampling rate dependence of amplitude-frequency correlation in EMG signals A. Siemier~ski 1, A. Kebel 1, P. Klajner 2. 1Biomechanics Laboratory, University
School of Physical Education, Wroclaw, Poland, 2University of Southern California, Los Angeles, USA, 3University of Technology, Wroclaw, Poland
School of Physical Education, Wroctaw, Poland, 2Faculty of Fundamental Problems of Technology, Wroctaw University of Technology, Wroctaw, Poland
Introduction: Neuromuscular fatigue is an important factor influencing performance. Frequency characteristics of electromyographic (EMG) signals reflect the underlying physiological processes related with muscle fatigue. We hypothesized that these physiological processes remain relatively constant between individuals. Thus, the aim of this study was to develop a mathematical model to characterize EMG signal frequency changes during a fatiguing isometric task. Methods: Twenty four healthy male subjects were recruited for this study. Surface EMG (Ag-AgCI, 1000Hz) of biceps brachii and force (isokinetic dynamometer, 100 Hz) data were recorded while the subjects performed a submaximal (80% of maximal voluntary contraction) isometric contraction of elbow flexors for 15 seconds. Zero-Crossing-Rate (ZCR) in every 1-second period was calculated to identify shifts in EMG frequency. Multiple linear regression was used to build a mathematical representation of frequency changes. Results: 1st order linear regression equation provided a good representation of EMG frequency shifts (r2 min = 0.47, r2 max = 0.93). Pearson's correlation revealed a statistically significant (r = -0.72, p < 0.05) relation between regression coefficients. An adjusted mathematical model, based on relationship between the regression coefficients, 151 and 150, was formulated to characterize EMG frequency. The maximum difference between calculated and observed values of ZCR for all subjects was 7.2%. Discussion: The mathematical model developed in this study provided an accurate description of frequency changes during submaximal isometric muscle contractions to fatigue. The observed relation between regression coefficients allowed modification of the equation to characterize subject-specific frequency changes by substituting initial frequency values specific to each individual. The results of this study may help to objectively assess level of fatigue during task performance in athletic and patient populations.
Introduction: The extent or onset of fatigue is typically assessed by simultaneous monitoring of frequency content and amplitude of surface EMG signals collected from engaged muscles. When activity level is kept unchanged with time, fatigue results in a downward shift of the frequency spectrum of the signal and an increase of signal amplitude. However, such negative amplitudefrequency correlations occur also at no fatigue due to varying signal to noise ratio, which could blur the interpretation of EMG signal behavior in terms of fatigue. Objective: To assess the magnitude of this effect and its dependence on the sampling rate of EMG signals. Material and Method: Nineteen healthy male subjects volunteered for the study. Surface EMG electrodes were used to record the activity of the vastus lateralis muscle during two-second-long trials of isometric activity corresponding to knee extending moments of 5%, 10%, 20%, 30% and 50% of MVC. The sequence of activity level was random to avoid the effects of fatigue. The EMG signals were originally sampled at a rate of 5 kHz and then processed off-line to simulate lower sampling rates ranging between 50 Hz and the original sampling rate. Two measures of amplitude (RMS and MAV (mean absolute value)) and two measures of dominating frequency (Fmed, Fmean) of the EMG signals were computed for each trial and for each simulated sampling rate. Results and Conclusions: Negative nonlinear Spearman and Kendall correlation coefficients between Fmed and RMS (MAV), as well as between Fmean and RMS (MAV) were found. The absolute value of these coefficients increased when the sampling rate increased, and the effect was especially noticeable for Fmean, even at sampling rates as low as 1 kHz. It is concluded that this correlation, which was found here at no fatigue, may be behind a part of the downward frequency shift normally attributed to fatigue. To minimize the effect, oversampling of EMG signals should be avoided.
7462 Fr, 12:15-12:30 (P51) The interplay of sensorimotor time-delays and noise in multisensory integration M. Venkadesan 1, M. Srinivasan 2, J. Guckenheimer 3, EJ. Valero-Cuevas 1.
7594 Fr, 14:00-14:15 (P52) An emg-driven model to investigate cocontraction of lower extremity muscles during gait
1Sibley School of Mechanical & Aerospace Engineering, Ithaca, NY, USA, 2 Theoretical and Applied Mechanics, Ithaca, NY, USA, 3Department of Mathematics, Cornell University, Ithaca, NY, USA
University of South Carolina, Charleston, USA, 2Mechanical Engineering, The Ohio State University, Columbus, USA, 3Industrial Engineering, North Carolina State University, Raleigh, USA
The dominant thinking in sensorimotor control is that minimizing effects of sensorimotor noise explains multisensory integration, referred to as the minimumvariance principle [1]. Contrarily, when we perform a sensorimotor task, such as handling an object, timely as well as accurate control is imperative so that we do not drop it. Hence, we hypothesized that multisensory integration should emerge from interplay of sensorimotor time-delays and noise. We tested our hypothesis by altering available sensory modalities (thumbpad sensation/vision) during a novel task of object manipulation at the boundary of instability, thus enabling us to model the system using bifurcation theory [2] that characterizes dynamics at a transition to instability. We asked 12 consenting subjects to maximally compress a slender spring using only their thumbpad without letting it slip. We modeled spring buckling dynamics as a subcritical pitchfork bifurcation [2] and sensorimotor control as multisensory proportional feedback with time-delays and noise. Both experimentally and computationally, the loss of thumbpad sensation significantly affected performance since this modality is both accurate and fast. Experimentally, lack of vision degraded performance only when thumbpad sensation was absent. Our model found that sensory weights emerging from a trade-off between time-delays and noise yielded better performance than the minimum-variance principle. Additionally an extra time-delay for non-digital mechanoreceptors over and above sensory and nerve-conduction delays was necessary to explain why lack of vision affected performance only when thumbpad sensation was absent. This additional delay is only due to computational time-delays. To our knowledge, our results show for the first time that, (i) neural computational time-delays can measurably affect performance and (ii) multisensory integration emerges from a trade-off between time-delays and noise. Work supported by NSF 0237258, NIH R21-HD048566 grants to FVC.
The purpose of this study was to introduce an EMG-driven neuromusculoskeletal model to predict cocontraction among individual musculotendon units that comprise the synergistic and antagonistic muscle groups involved in knee flexion/extension during normal walking gait. Description of the muscle's mechanical response was based on Hill's and Zajac's work, but incorporated individual muscle length, velocity, and excitation considerations. Processed EMG represented the muscle's neural input. A musculoskeletal model defining joint kinematics, muscle line of action and architecture of the left lower limb was developed by using SIMM [1] and modifying its associated model. Muscle kinematics were then calculated in conjunction with three-dimensional motion data. Computer simulations combined with a novel calibration procedure which simulated the knee-hip and knee-ankle interactions during gait allowed the estimation of all model parameters. Muscle force profiles were predicted for selected muscles of the lower limb using four subjects during normal walking. Model validation was performed against inverse dynamics net joint moments. A new validation approach was performed through estimated muscle gains, which in our study were within physiological range, and a good match was obtained between moment curves. Correlations ranged between 0.73~).97 for the gait trials and RMS differences between 22.2-12.99 Nm. The results were similar or better than those previously reported. Cocontraction indexes were higher during events in the gait cycle that required stability and control at the joint. This model provides a solid foundation for further improvements that will be discussed. The results support the feasibility of using the proposed model, as a potential solution to the interdeterminancy problem providing solutions to muscle forces involved in normal human movement, and a cocontraction index to assess muscle balance during gait.
References [1] Ernst M.O., Banks M.S. Nature 2002; 415: 429-433. [2] Guckenheimer J., Holmes P. 2002; Springer, New York.
References [1] S. Delp, P. Loan. A graphics-based software system to develop and analyze models of musculoskeletal structures. Comput. Biol. Med. 1995; 25: 21-34.
T. Karakostas 1, N. Berme 2, S. Hsiang 3 . 1Motion Analysis Laboratory, Medical