Sridhar Arjunan - Academia.edu (original) (raw)

Papers by Sridhar Arjunan

PloS one, 2017

This study has described and experimentally validated the differential electrodes surface electro... more This study has described and experimentally validated the differential electrodes surface electromyography (sEMG) model for tibialis anterior muscles during isometric contraction. This model has investigated the effect of pennation angle on the simulated sEMG signal. The results show that there is no significant effect of pennation angle in the range 0° to 20° to the single fibre action potential shape recorded on the skin surface. However, the changes with respect to pennation angle are observed in sEMG amplitude, frequency and fractal dimension. It is also observed that at different levels of muscle contractions there is similarity in the relationships with Root Mean Square, Median Frequency, and Fractal Dimension of the recorded and simulated sEMG signals.

Conference proceedings: ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conference

This paper reports research conducted to evaluate the use of sparse ICA for the separation of mus... more This paper reports research conducted to evaluate the use of sparse ICA for the separation of muscle activity from SEMG. It discusses some of the conditions that could affect the reliability of the separation and evaluates issues related to the properties of the signals and number of sources. The paper reports tests using Zibulevsky's method of temporal plotting to identify number of independent sources in SEMG recordings. The theoretical analysis and experimental results demonstrate that sparse ICA is not suitable for SEMG signals. The results identify that the technique is unable to identify finite number of active muscles. The work demonstrates that even at extremely low level of muscle contraction, and with filtering using wavelets and band pass filters, it is not possible to get the data sparse enough to identify number of independent sources using Zibulevsky's sparse decomposition technique.

2013 ISSNIP Biosignals and Biorobotics Conference: Biosignals and Robotics for Better and Safer Living (BRC), 2013

ABSTRACT It has been observed that there is a loss of complexity in the human body as we age. Thi... more ABSTRACT It has been observed that there is a loss of complexity in the human body as we age. This is more prominent in the muscle strength and activity. Surface Electromyogram (sEMG) reflects the strength of muscle contraction. This age related changes in sEMG have been associated with a reduction in the number of muscle fibers and a drop in the ratio of type II muscle fibers. In this study, we have modified our existing EMG model by populating lifelike parameters which is related to the changes in the muscle due to age. In order to verify and identify the reasons for these changes, experiments were conducted on subjects belonging to younger (20-29 years) and older (61-69) age groups. Fractal dimension of sEMG, a measure of complexity was computed for both experimental and simulated sEMG signal. Results show that there was significant change in the fractal dimension of sEMG and this change was observed in both experimental and simulated sEMG. This study has developed a model to observe the changes in the muscle activity as the age progress, which can be useful in analyzing the neuromuscular disorders due to age.

2013 ISSNIP Biosignals and Biorobotics Conference: Biosignals and Robotics for Better and Safer Living (BRC), 2013

ABSTRACT Neuromuscular control consists of central command and the peripheral neuromuscular syste... more ABSTRACT Neuromuscular control consists of central command and the peripheral neuromuscular system (PNS). The PNS has a feedback loop and consists of the spindle receptors that respond to the stretch of the muscle and is responsible for maintaining steady contraction. Stretch reflex is commonly used to determine the properties of the PNS, and the stretch reflex latencies are attributable to the neural delays. While the importance of spindle feedback in muscle control has been hypothesised and reported, no significant research has been conducted to study the relationship of neural delays and muscle activity. This paper presents a study to test the hypothesis that the neural delays in the PNS are the cause of modulation of the electrical activity of the muscle. While the continuous variation of surface electromyogram (sEMG) during isometric contraction has been previously observed, and the amplitude of this variation has been studied, the time constant associated with this variation has not yet been studied. This paper proposes the hypothesis that the time constant of the variation of sEMG during isometric contraction is associated with the neural delays in the PNS.

2013 ISSNIP Biosignals and Biorobotics Conference: Biosignals and Robotics for Better and Safer Living (BRC), 2013

ABSTRACT Classification of the biometrics data for identity validation can be considered as a sin... more ABSTRACT Classification of the biometrics data for identity validation can be considered as a single-class problem. Each class can be represented by a unique set of features. However, current feature selection techniques consider the entire database and identify the feature-set that is suitable for representing all available classes. This may not be the best representation of the biometrics data of each individual because different people may have difference in the most suitable features to represent their biometric data. In this study, a class-specific dynamic feature selection method has been proposed and experimentally validated using dynamic signatures. This method is based on the variance within the feature set, where the features with smaller variance are selected and the ones with larger variance are rejected. A comparison was made with other feature selection methods, and the results show that there were differences in the features representing different classes. A significant improvement in the classification accuracy and specificity and sensitivity was also observed when using this feature selection technique.

Biomedizinische Technik. Biomedical engineering, Jan 17, 2015

Identifying functional handgrip patterns using surface electromygram (sEMG) signal recorded from ... more Identifying functional handgrip patterns using surface electromygram (sEMG) signal recorded from amputee residual muscle is required for controlling the myoelectric prosthetic hand. In this study, we have computed the signal fractal dimension (FD) and maximum fractal length (MFL) during different grip patterns performed by healthy and transradial amputee subjects. The FD and MFL of the sEMG, referred to as the fractal features, were classified using twin support vector machines (TSVM) to recognize the handgrips. TSVM requires fewer support vectors, is suitable for data sets with unbalanced distributions, and can simultaneously be trained for improving both sensitivity and specificity. When compared with other methods, this technique resulted in improved grip recognition accuracy, sensitivity, and specificity, and this improvement was significant (κ=0.91).

Medical & biological engineering & computing, Jan 30, 2015

Reduction in number of motor units (nMU) and fast fibre ratio (FFR) is associated with disease or... more Reduction in number of motor units (nMU) and fast fibre ratio (FFR) is associated with disease or atrophy when this is rapid. There is a need to study the effect of nMU and FFR to analyse the association with ageing and disease. This study has developed a mathematical model to investigate the relationship between nMU and FFR on surface electromyogram (sEMG) of the biceps muscles. The model has been validated by comparing the simulation outcomes with experiments comparing the sEMG of physically active younger and older cohort. The results show that there is statistically significant difference between the two groups, and the simulation studies closely model the experimental results. This model can be applied to identify the cause of muscle weakness among the elderly due to factors such as muscle dystrophy or preferential loss of type F muscle fibres.

International Conference on Computer Graphics, Imaging and Visualisation (CGIV'06), 2006

Abstract This paper describes a new technique for recognizing speech using visual speech informat... more Abstract This paper describes a new technique for recognizing speech using visual speech information. The video data of the speaker's mouth is represented using grayscale im-ages named as motion history image (MHI). MHI is gener-ated by applying accumulative image ...

Biomedical engineering online, 2015

Myoelectric controlled prosthetic hand requires machine based identification of hand gestures usi... more Myoelectric controlled prosthetic hand requires machine based identification of hand gestures using surface electromyogram (sEMG) recorded from the forearm muscles. This study has observed that a sub-set of the hand gestures have to be selected for an accurate automated hand gesture recognition, and reports a method to select these gestures to maximize the sensitivity and specificity. Experiments were conducted where sEMG was recorded from the muscles of the forearm while subjects performed hand gestures and then was classified off-line. The performances of ten gestures were ranked using the proposed Positive-Negative Performance Measurement Index (PNM), generated by a series of confusion matrices. When using all the ten gestures, the sensitivity and specificity was 80.0% and 97.8%. After ranking the gestures using the PNM, six gestures were selected and these gave sensitivity and specificity greater than 95% (96.5% and 99.3%); Hand open, Hand close, Little finger flexion, Ring fing...

2013 ISSNIP Biosignals and Biorobotics Conference: Biosignals and Robotics for Better and Safer Living (BRC), 2013

This work presents a myoelectric control scheme of a virtual hand based on higher order statistic... more This work presents a myoelectric control scheme of a virtual hand based on higher order statistics. The main objective of the experiments is to benefit the general amputee population, offering a training tool to potential prosthesis users. A simple and direct input control is based on the residual SEMG signals from the upper limb of amputee people. The third-order cumulant features are calculated recursively and a non-threshold control scheme is implemented online in a virtual hand. These SEMG features open and close the virtual hand assisted by the corresponding user's visual feedback. Seven experiments were concluded satisfactorily by an amputee volunteer.

2013 ISSNIP Biosignals and Biorobotics Conference: Biosignals and Robotics for Better and Safer Living (BRC), 2013

This paper presents the classification of motor tasks, using surface electromyography (sEMG) to c... more This paper presents the classification of motor tasks, using surface electromyography (sEMG) to control a prosthetic hand for rehabilitation of amputees. Two types of classifiers are compared: k-Nearest Neighbor (k-NN) and Bayesian (Discriminant Analysis). Motor tasks are divided into four groups correlated. The volunteers were healthy people (without amputation) and several analyzes of each of the signals were conducted. The online simulations use the sliding window technique and for feature extraction RMS (Root Mean Square), VAR (Variance) and WL (Waveform Length) values were used. A model is proposed for reclassification using cross-validation in order to validate the classification, and a visualization in Sammon Maps is provided in order to observe the separation of the classes for each set of motor tasks. Finally, the proposed method can be implemented in a computer interface providing a visual feedback through a artificial prosthetic developed in Visual C++ and MATLAB commands.

9th Biennial Conference of the Australian Pattern Recognition Society on Digital Image Computing Techniques and Applications (DICTA 2007), 2007

This research reports the recognition of facial movements during unvoiced speech and the identifi... more This research reports the recognition of facial movements during unvoiced speech and the identification of hand gestures using surface Electromyogram (sEMG). The paper proposes two different methods for identifying facial movements and hand gestures, which can be useful for providing simple commands and control to computer, an important application of HCI. Experimental results demonstrate that the features of sEMG recordings are suitable for characterising the muscle activation during unvoiced speech and subtle gestures. The scatter plots from the two methods demonstrate the separation of data for each corresponding vowel and each hand gesture. The results indicate that there is small inter-experimental variation but there are large intersubject variations. This inter-subject variation may be attributable to anatomical differences and different speed and style of speaking for the different subjects. The proposed system provides better results when is trained and tested by individual user. The possible applications of this research include giving simple commands to computer for disabled, developing prosthetic hands, use of classifying sEMG for HCI based systems. Digital Image Computing Techniques and Applications 0-7695-3067-2/07 $25.00

TENCON 2008 - 2008 IEEE Region 10 Conference, 2008

This paper presents a silent speech recognition technique based on facial muscle activity and vid... more This paper presents a silent speech recognition technique based on facial muscle activity and video, without evaluating any voice signals. This research examines the use of facial surface electromyogram (SEMG) to identify unvoiced vowels and vision-based technique to classify unvoiced consonants. The moving root mean square (RMS) of SEMG signals of four facial muscles is used to segment the signals and to identify the start and end of a silently spoken vowels. Visual features are extracted from the mouth video of a speaker silently uttering consonants using motion segmentation and image moment techniques. The SEMG features and visual features are classified using feedforward multilayer perceptron (MLP) neural networks. The preliminary results demonstrate that the proposed technique yields high recognition rate for classification of unvoiced vowels using SEMG features. Similarly, promising results are obtained in identification of consonants using visual features. The results demonstrate that the system is easy to train for a new user and suggest that such a system works reliably for voiceless, simple speech based commands for human computer interface when it is trained for a user.

TENCON 2008 - 2008 IEEE Region 10 Conference, 2008

This research paper reports the use of fractal features based technique in physiological signals ... more This research paper reports the use of fractal features based technique in physiological signals like surface electromyogram (sEMG), electroencephalogram (EEG) which has gained increasing attention in biosignal processing for medical and healthcare applications. This research reports the use of fractal dimension, a fractal complexity measure in physiological signals and also reports identification of a new feature of sEMG, maximum fractal

International Journal of Signal and Imaging Systems Engineering, 2014

2012 ISSNIP Biosignals and Biorobotics Conference: Biosignals and Robotics for Better and Safer Living (BRC), 2012

ABSTRACT

Signal, Image and Video Processing, 2013

ABSTRACT It is well known that there is a change in the spectrum of surface electromyogram (sEMG)... more ABSTRACT It is well known that there is a change in the spectrum of surface electromyogram (sEMG) with the onset of muscle fatigue. This change has largely been attributed to the change in muscle conduction velocity. We have investigated this theory by using our previously developed sEMG model with the various neuromuscular parameters. The change of spectrum of the experimental results has been compared with simulated sEMG in response to the change in muscle conduction velocity using similar conditions. In this study, the designed model implemented the change in the muscle conduction velocity ( Deltav\Delta vDeltav ) as a parameter to simulate the sEMG. The shift in spectrum was identified by computation of the median frequency (MDF) of the sEMG before and after the implementation of the rate of the slow down (RS or Deltav\Delta vDeltav ) parameter in the model. The results based on the simulation of the model show that the rate of change in MDF was consistent in all levels of contractions and it is not same with experimental conditions. The results also suggest that the large change in MDF in the simulated sEMG than in experimental conditions may be attributable to other factors such as change in the recruitment pattern and the level of contraction not only due to the change in the muscle conduction velocity ( Deltav\Delta vDeltav ).

PloS one, 2017

This study has described and experimentally validated the differential electrodes surface electro... more This study has described and experimentally validated the differential electrodes surface electromyography (sEMG) model for tibialis anterior muscles during isometric contraction. This model has investigated the effect of pennation angle on the simulated sEMG signal. The results show that there is no significant effect of pennation angle in the range 0° to 20° to the single fibre action potential shape recorded on the skin surface. However, the changes with respect to pennation angle are observed in sEMG amplitude, frequency and fractal dimension. It is also observed that at different levels of muscle contractions there is similarity in the relationships with Root Mean Square, Median Frequency, and Fractal Dimension of the recorded and simulated sEMG signals.

Conference proceedings: ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conference

This paper reports research conducted to evaluate the use of sparse ICA for the separation of mus... more This paper reports research conducted to evaluate the use of sparse ICA for the separation of muscle activity from SEMG. It discusses some of the conditions that could affect the reliability of the separation and evaluates issues related to the properties of the signals and number of sources. The paper reports tests using Zibulevsky's method of temporal plotting to identify number of independent sources in SEMG recordings. The theoretical analysis and experimental results demonstrate that sparse ICA is not suitable for SEMG signals. The results identify that the technique is unable to identify finite number of active muscles. The work demonstrates that even at extremely low level of muscle contraction, and with filtering using wavelets and band pass filters, it is not possible to get the data sparse enough to identify number of independent sources using Zibulevsky's sparse decomposition technique.

2013 ISSNIP Biosignals and Biorobotics Conference: Biosignals and Robotics for Better and Safer Living (BRC), 2013

ABSTRACT It has been observed that there is a loss of complexity in the human body as we age. Thi... more ABSTRACT It has been observed that there is a loss of complexity in the human body as we age. This is more prominent in the muscle strength and activity. Surface Electromyogram (sEMG) reflects the strength of muscle contraction. This age related changes in sEMG have been associated with a reduction in the number of muscle fibers and a drop in the ratio of type II muscle fibers. In this study, we have modified our existing EMG model by populating lifelike parameters which is related to the changes in the muscle due to age. In order to verify and identify the reasons for these changes, experiments were conducted on subjects belonging to younger (20-29 years) and older (61-69) age groups. Fractal dimension of sEMG, a measure of complexity was computed for both experimental and simulated sEMG signal. Results show that there was significant change in the fractal dimension of sEMG and this change was observed in both experimental and simulated sEMG. This study has developed a model to observe the changes in the muscle activity as the age progress, which can be useful in analyzing the neuromuscular disorders due to age.

2013 ISSNIP Biosignals and Biorobotics Conference: Biosignals and Robotics for Better and Safer Living (BRC), 2013

ABSTRACT Neuromuscular control consists of central command and the peripheral neuromuscular syste... more ABSTRACT Neuromuscular control consists of central command and the peripheral neuromuscular system (PNS). The PNS has a feedback loop and consists of the spindle receptors that respond to the stretch of the muscle and is responsible for maintaining steady contraction. Stretch reflex is commonly used to determine the properties of the PNS, and the stretch reflex latencies are attributable to the neural delays. While the importance of spindle feedback in muscle control has been hypothesised and reported, no significant research has been conducted to study the relationship of neural delays and muscle activity. This paper presents a study to test the hypothesis that the neural delays in the PNS are the cause of modulation of the electrical activity of the muscle. While the continuous variation of surface electromyogram (sEMG) during isometric contraction has been previously observed, and the amplitude of this variation has been studied, the time constant associated with this variation has not yet been studied. This paper proposes the hypothesis that the time constant of the variation of sEMG during isometric contraction is associated with the neural delays in the PNS.

2013 ISSNIP Biosignals and Biorobotics Conference: Biosignals and Robotics for Better and Safer Living (BRC), 2013

ABSTRACT Classification of the biometrics data for identity validation can be considered as a sin... more ABSTRACT Classification of the biometrics data for identity validation can be considered as a single-class problem. Each class can be represented by a unique set of features. However, current feature selection techniques consider the entire database and identify the feature-set that is suitable for representing all available classes. This may not be the best representation of the biometrics data of each individual because different people may have difference in the most suitable features to represent their biometric data. In this study, a class-specific dynamic feature selection method has been proposed and experimentally validated using dynamic signatures. This method is based on the variance within the feature set, where the features with smaller variance are selected and the ones with larger variance are rejected. A comparison was made with other feature selection methods, and the results show that there were differences in the features representing different classes. A significant improvement in the classification accuracy and specificity and sensitivity was also observed when using this feature selection technique.

Biomedizinische Technik. Biomedical engineering, Jan 17, 2015

Identifying functional handgrip patterns using surface electromygram (sEMG) signal recorded from ... more Identifying functional handgrip patterns using surface electromygram (sEMG) signal recorded from amputee residual muscle is required for controlling the myoelectric prosthetic hand. In this study, we have computed the signal fractal dimension (FD) and maximum fractal length (MFL) during different grip patterns performed by healthy and transradial amputee subjects. The FD and MFL of the sEMG, referred to as the fractal features, were classified using twin support vector machines (TSVM) to recognize the handgrips. TSVM requires fewer support vectors, is suitable for data sets with unbalanced distributions, and can simultaneously be trained for improving both sensitivity and specificity. When compared with other methods, this technique resulted in improved grip recognition accuracy, sensitivity, and specificity, and this improvement was significant (κ=0.91).

Medical & biological engineering & computing, Jan 30, 2015

Reduction in number of motor units (nMU) and fast fibre ratio (FFR) is associated with disease or... more Reduction in number of motor units (nMU) and fast fibre ratio (FFR) is associated with disease or atrophy when this is rapid. There is a need to study the effect of nMU and FFR to analyse the association with ageing and disease. This study has developed a mathematical model to investigate the relationship between nMU and FFR on surface electromyogram (sEMG) of the biceps muscles. The model has been validated by comparing the simulation outcomes with experiments comparing the sEMG of physically active younger and older cohort. The results show that there is statistically significant difference between the two groups, and the simulation studies closely model the experimental results. This model can be applied to identify the cause of muscle weakness among the elderly due to factors such as muscle dystrophy or preferential loss of type F muscle fibres.

International Conference on Computer Graphics, Imaging and Visualisation (CGIV'06), 2006

Abstract This paper describes a new technique for recognizing speech using visual speech informat... more Abstract This paper describes a new technique for recognizing speech using visual speech information. The video data of the speaker's mouth is represented using grayscale im-ages named as motion history image (MHI). MHI is gener-ated by applying accumulative image ...

Biomedical engineering online, 2015

Myoelectric controlled prosthetic hand requires machine based identification of hand gestures usi... more Myoelectric controlled prosthetic hand requires machine based identification of hand gestures using surface electromyogram (sEMG) recorded from the forearm muscles. This study has observed that a sub-set of the hand gestures have to be selected for an accurate automated hand gesture recognition, and reports a method to select these gestures to maximize the sensitivity and specificity. Experiments were conducted where sEMG was recorded from the muscles of the forearm while subjects performed hand gestures and then was classified off-line. The performances of ten gestures were ranked using the proposed Positive-Negative Performance Measurement Index (PNM), generated by a series of confusion matrices. When using all the ten gestures, the sensitivity and specificity was 80.0% and 97.8%. After ranking the gestures using the PNM, six gestures were selected and these gave sensitivity and specificity greater than 95% (96.5% and 99.3%); Hand open, Hand close, Little finger flexion, Ring fing...

2013 ISSNIP Biosignals and Biorobotics Conference: Biosignals and Robotics for Better and Safer Living (BRC), 2013

This work presents a myoelectric control scheme of a virtual hand based on higher order statistic... more This work presents a myoelectric control scheme of a virtual hand based on higher order statistics. The main objective of the experiments is to benefit the general amputee population, offering a training tool to potential prosthesis users. A simple and direct input control is based on the residual SEMG signals from the upper limb of amputee people. The third-order cumulant features are calculated recursively and a non-threshold control scheme is implemented online in a virtual hand. These SEMG features open and close the virtual hand assisted by the corresponding user's visual feedback. Seven experiments were concluded satisfactorily by an amputee volunteer.

2013 ISSNIP Biosignals and Biorobotics Conference: Biosignals and Robotics for Better and Safer Living (BRC), 2013

This paper presents the classification of motor tasks, using surface electromyography (sEMG) to c... more This paper presents the classification of motor tasks, using surface electromyography (sEMG) to control a prosthetic hand for rehabilitation of amputees. Two types of classifiers are compared: k-Nearest Neighbor (k-NN) and Bayesian (Discriminant Analysis). Motor tasks are divided into four groups correlated. The volunteers were healthy people (without amputation) and several analyzes of each of the signals were conducted. The online simulations use the sliding window technique and for feature extraction RMS (Root Mean Square), VAR (Variance) and WL (Waveform Length) values were used. A model is proposed for reclassification using cross-validation in order to validate the classification, and a visualization in Sammon Maps is provided in order to observe the separation of the classes for each set of motor tasks. Finally, the proposed method can be implemented in a computer interface providing a visual feedback through a artificial prosthetic developed in Visual C++ and MATLAB commands.

9th Biennial Conference of the Australian Pattern Recognition Society on Digital Image Computing Techniques and Applications (DICTA 2007), 2007

This research reports the recognition of facial movements during unvoiced speech and the identifi... more This research reports the recognition of facial movements during unvoiced speech and the identification of hand gestures using surface Electromyogram (sEMG). The paper proposes two different methods for identifying facial movements and hand gestures, which can be useful for providing simple commands and control to computer, an important application of HCI. Experimental results demonstrate that the features of sEMG recordings are suitable for characterising the muscle activation during unvoiced speech and subtle gestures. The scatter plots from the two methods demonstrate the separation of data for each corresponding vowel and each hand gesture. The results indicate that there is small inter-experimental variation but there are large intersubject variations. This inter-subject variation may be attributable to anatomical differences and different speed and style of speaking for the different subjects. The proposed system provides better results when is trained and tested by individual user. The possible applications of this research include giving simple commands to computer for disabled, developing prosthetic hands, use of classifying sEMG for HCI based systems. Digital Image Computing Techniques and Applications 0-7695-3067-2/07 $25.00

TENCON 2008 - 2008 IEEE Region 10 Conference, 2008

This paper presents a silent speech recognition technique based on facial muscle activity and vid... more This paper presents a silent speech recognition technique based on facial muscle activity and video, without evaluating any voice signals. This research examines the use of facial surface electromyogram (SEMG) to identify unvoiced vowels and vision-based technique to classify unvoiced consonants. The moving root mean square (RMS) of SEMG signals of four facial muscles is used to segment the signals and to identify the start and end of a silently spoken vowels. Visual features are extracted from the mouth video of a speaker silently uttering consonants using motion segmentation and image moment techniques. The SEMG features and visual features are classified using feedforward multilayer perceptron (MLP) neural networks. The preliminary results demonstrate that the proposed technique yields high recognition rate for classification of unvoiced vowels using SEMG features. Similarly, promising results are obtained in identification of consonants using visual features. The results demonstrate that the system is easy to train for a new user and suggest that such a system works reliably for voiceless, simple speech based commands for human computer interface when it is trained for a user.

TENCON 2008 - 2008 IEEE Region 10 Conference, 2008

This research paper reports the use of fractal features based technique in physiological signals ... more This research paper reports the use of fractal features based technique in physiological signals like surface electromyogram (sEMG), electroencephalogram (EEG) which has gained increasing attention in biosignal processing for medical and healthcare applications. This research reports the use of fractal dimension, a fractal complexity measure in physiological signals and also reports identification of a new feature of sEMG, maximum fractal

International Journal of Signal and Imaging Systems Engineering, 2014

2012 ISSNIP Biosignals and Biorobotics Conference: Biosignals and Robotics for Better and Safer Living (BRC), 2012

ABSTRACT

Signal, Image and Video Processing, 2013

ABSTRACT It is well known that there is a change in the spectrum of surface electromyogram (sEMG)... more ABSTRACT It is well known that there is a change in the spectrum of surface electromyogram (sEMG) with the onset of muscle fatigue. This change has largely been attributed to the change in muscle conduction velocity. We have investigated this theory by using our previously developed sEMG model with the various neuromuscular parameters. The change of spectrum of the experimental results has been compared with simulated sEMG in response to the change in muscle conduction velocity using similar conditions. In this study, the designed model implemented the change in the muscle conduction velocity ( Deltav\Delta vDeltav ) as a parameter to simulate the sEMG. The shift in spectrum was identified by computation of the median frequency (MDF) of the sEMG before and after the implementation of the rate of the slow down (RS or Deltav\Delta vDeltav ) parameter in the model. The results based on the simulation of the model show that the rate of change in MDF was consistent in all levels of contractions and it is not same with experimental conditions. The results also suggest that the large change in MDF in the simulated sEMG than in experimental conditions may be attributable to other factors such as change in the recruitment pattern and the level of contraction not only due to the change in the muscle conduction velocity ( Deltav\Delta vDeltav ).