J.S. Karlsson

Selective activation of neuromuscular compartments within the human trapezius muscle

Task-dependent differences in relative activity between functional subdivisions within human muscles are well documented. Contrary, independent voluntary control of anatomical subdivisions, termed neuromuscular compartments is not observed in human muscles. Therefore, the main aim of this study was to investigate whether subdivisions within the human trapezius can be independently activated by voluntary command using biofeedback guidance. Bipolar electromyographical electrodes were situated on four subdivisions of the trapezius muscle. The threshold for active and rest for each subdivision was set to >12% and <1.5% of the maximal electromyographical amplitude recorded during a maximal voluntary contraction. After 1h with biofeedback from each of the four trapezius subdivisions, 11 of 15 subjects learned selective activation of at least one of the four anatomical subdivisions of the trapezius muscle. All subjects managed to voluntarily activate the lower subdivisions independently from the upper subdivisions. Half of the subjects succeeded to voluntarily activate both upper subdivisions independently from the two lower subdivisions. These findings show that anatomical subdivisions of the human trapezius muscle can be independently activated by voluntary command, indicating neuromuscular compartmentalization of the trapezius muscle. The independent activation of the upper and lower subdivisions of the trapezius is in accordance with the selective innervation by the fine cranial and main branch of the accessory nerve to the upper and lower subdivisions. These findings provide new insight into motor control characteristics, learning possibilities, and function of the clinically relevant human trapezius muscle.

An estimation of the influence of force decrease on the mean power spectral frequency shift of the EMG during repetitive maximum dynamic knee extensions

Frequency analysis of myoelectric (ME) signals, using the mean power spectral frequency (MNF), has been widely used to characterize peripheral muscle fatigue during isometric contractions assuming constant force. However, during repetitive isokinetic contractions performed with maximum effort, output (force or torque) will decrease markedly during the initial 40-60 contractions, followed by a phase with little or no change. MNF shows a similar pattern. In situations where there exist a significant relationship between MNF and output, part of the decrease in MNF may per se be related to the decrease in force during dynamic contractions. This study estimated force effects on the MNF shifts during repetitive dynamic knee extensions. Twenty healthy volunteers participated in the study and both surface ME signals (from the right vastus lateralis, vastus medialis, and rectus femoris muscles) and the biomechanical signals (force, position, and velocity) of an isokinetic dynamometer were measured. Two tests were performed: (i) 100 repetitive maximum isokinetic contractions of the right knee extensors, and (ii) five gradually increasing static knee extensions before and after (i). The corresponding ME signal time-frequency representations were calculated using the continuous wavelet transform. Compensation of the MNF variables of the repetitive contractions was performed with respect to the individual MNF-force relation based on an average of five gradually increasing contractions. Whether or not compensation was necessary was based on the shape of the MNF-force relationship. A significant compensation of the MNF was found for the repetitive isokinetic contractions. In conclusion, when investigating maximum dynamic contractions, decreases in MNF can be due to mechanisms similar to those found during sustained static contractions (force-independent component of fatigue) and in some subjects due to a direct effect of the change in force (force-dependent component of fatigue). In order to compare MNF shifts during sustained static and repetitive dynamic contractions it is necessary to estimate the force-dependent component of fatigue of dynamic contractions. Our results are preliminary and have to be confirmed in larger experiments using single dynamic contractions when determining the MNF-force relationship of the unfatigued situation.

Motor unit synchronization during fatigue: Described with a novel sEMG method based on large motor unit samples

The amount of documented increase in motor unit (MU) synchronization with fatigue and its possible relation with force tremor varies largely, possibly due to inhomogeneous muscle activation and methodological discrepancies and limitations. The aim of this study was to apply a novel surface electromyographical (EMG) descriptor for MU synchronization based on large MU populations to examine changes in MU synchronization with fatigue at different sites of a muscle and its relation to tremor. Twenty-four subjects performed an isometric elbow flexion at 25% of maximal voluntary contraction until exhaustion. Monopolar EMG signals were recorded using a grid of 130 electrodes above the biceps brachii. Changes in MU synchronization were estimated based on the sub-band skewness of EMG signals and tremor by the coefficient of variation in force. The synchronization descriptor was dependent on recording site and increased with fatigue together with tremor. There was a general association between these two parameters, but not between their fluctuations. These results are in agreement with other surface EMG studies and indicate that the novel descriptor can be used to attain information of synchronization between large MU populations during fatigue that cannot be retrieved with intra-muscular EMG.

Chronic whiplash associated disorders and neck movement measurements: an instantaneous helical axis approach

This paper presents an assessment tool for objective neck movement analysis of subjects suffering from chronic whiplash-associated disorders (WAD). Three-dimensional (3-D) motion data is collected by a commercially available motion analysis system. Head rotation, defined in this paper as the rotation angle around the instantaneous helical axis (IHA), is used for extracting a number of variables (e.g., angular velocity and range, symmetry of motion). Statistically significant differences were found between controls and subjects with chronic WAD in a number of variables.

Adaptive spatial filtering of multichannel surface electromyogram signals

Spatial filtering of surface electromyography (EMG) signal can be used to enhance single motor unit action potentials (MUAPs). Traditional spatial filters for surface EMG do not take into consideration that some electrodes could have poor skin contact. In contrast to the traditional a priori defined filters, this study introduces an adaptive spatial filtering method that adapts to the signal characteristics. The adaptive filter, the maximum kurtosis filter (MKF), was obtained by using the linear combination of surrounding channels that maximises kurtosis. The MKF and conventional filters were applied to simulated EMG signals and to real EMG signals recorded with an electrode grid to evaluate their performance in detecting single motor units. The MKF was compared with conventional spatial filtering methods. Simulated signals, with different levels of spatially correlated noise, were used for comparison. The influence of one electrode with poor skin contact was also investigated. The MKF was found to be considerably better at enhancing a single MUAP than conventional methods for all levels of spatial correlation of the noise. For a spatial correlation of 0.97 of the noise, the improvement in the signal-to-noise ratio, where a MUAP could be detected, was at least 6 dB. With a simulated poor skin contact for one electrode, the improvement over the other methods was at least 19 dB.

Differential activation of regions within the biceps brachii muscle during fatigue

Aim:u2002 To examine the occurrence of repeated differential activation between the heads of the biceps brachii muscle and its relation to fatigue prevention during a submaximal contraction.

Simultaneous estimation of muscle fibre conduction velocity and muscle fibre orientation using 2D multichannel surface electromyogram.

The paper presents a new approach for simultaneous estimation of muscle fibre conduction velocity (MFCV) and muscle fibre orientation (MFO) for motor units (MUs) in two-dimensional (2D) multichannel surface electromyography recordings. This is an important tool for detecting changes and abnormalities in muscle function and structure. In addition, simultaneous estimation of MFO and MFCV avoids the necessity of manual electrode alignment. The proposed method detected propagating MU action potentials (MUAPs) in a running time window as moving components in amplitude maps. Thereafter, estimations were obtained by fitting a three-dimensional function to these maps. The performance was evaluated using synthetic MU signals at 10 dB SNR and authentic biceps brachii measurements. Results demonstrated MFCV and MFO estimates with standard deviations of less than 0.05 m s−1 and 1° for simulated signals, and less than 0.2 m s−1 and 4° for experimental data. However, standard deviations as low as 0.12 m s−1 and 1.6° from real signals were demonstrated. It was concluded that the method performs as well as, or better than, linear array multichannel methods when individual propagating MUAPs can be identified, even if electrodes are not aligned with fibre direction.

On-line signal quality estimation of multichannel surface electromyograms.

When multichannel surface-electromyography (MCSEMG) systems are used, there is a risk of recording low-quality signals. Such signals can be confusing for analysis and interpretation and can be caused by power-line interference, motion artifacts or poor electrode-skin contact. Usually, the electrode-skin impedance is measured to estimate the quality of the contact between the electrodes and the skin. However, this is not always practical, and the contact can change over short time-scales. A fast method is described to estimate the quality of individual signals of monopolar MCSEMG recordings based on volume conduction of myo-electric signals. The characteristics of the signals were described using two descriptor variables. Outliers (extreme data points) were detected in the two-dimensional distributions of the descriptor variables using a non-parametric technique, and the quality of the signals was estimated by their outlier probabilities. The methods performance was evaluated using 1 s long signals visually classified as very poor (G1), poor (G2) or good quality (G3). Recordings from different subjects, contraction levels and muscles were used. An optimum threshold at 0.05 outlier probability was proposed and resulted in classification accuracies of 100% and >70% for G1 and G2 signals, respectively, whereas <5% of the G3 signals were classified as poor. In conclusion, the proposed method estimated MCSEMG signal quality with high accuracy, compared with visual assessment, and is suitable for on-line implementation. The method could be applied to other multichannel sensor systems, with an arbitrary number of descriptor variables, when their distributions can be assumed to lie within a certain range.

Classification of neck movement patterns related to whiplash-associated disorders using neural networks

This paper presents a new method for classification of neck movement patterns related to whiplash-associated disorders (WAD) using a resilient backpropagation neural network (BPNN). WAD are a common diagnosis after neck trauma, typically caused by rear-end car accidents. Since physical injuries seldom are found with present imaging techniques, the diagnosis can be difficult to make. The active range of the neck is often visually inspected in patients with neck pain, but this is a subjective measure, and a more objective decision support system, that gives a reliable and more detailed analysis of neck movement pattern, is needed. The objective of this study was to evaluate the predictive ability of a BPNN, using neck movement variables as input. Three-dimensional (3-D) neck movement data from 59 subjects with WAD and 56 control subjects were collected with a ProReflex system. Rotation angle and angle velocity were calculated using the instantaneous helical axis method and motion variables were extracted. A principal component analysis was performed in order to reduce data and improve the BPNN performance. BPNNs with six hidden nodes had a predictivity of 0.89, a sensitivity of 0.90 and a specificity of 0.88, which are very promising results. This shows that neck movement analysis combined with a neural network could build the basis of a decision support system for classifying suspected WAD, even though further evaluation of the method is needed.

Duration of differential activations is functionally related to fatigue prevention during low-level contractions

The aim of this study was to investigate the importance of duration of differential activations between the heads of the biceps brachii on local fatigue during prolonged low-level contractions. Fifteen subjects carried out isometric elbow flexion at 5% of maximal voluntary contraction (MVC) for 30 min. MVCs were performed before and at the end of the prolonged contraction. Surface electromyographic (EMG) signals were recorded from both heads of the biceps brachii. Differential activation was analysed based on the difference in EMG amplitude (activation) between electrodes situated at the two heads. Differential activations were quantified by the power spectral median frequency of the difference in activation between the heads throughout the contraction. The inverse of the median frequency was used to describe the average duration of the differential activations. The relation between average duration of the differential activations and the fatigue-induced reduction in maximal force was explored by linear regression analysis. The main finding was that the average duration of differential activation was positively associated to relative maximal force at the end of the 30 min contraction (R(2)=0.5, P<0.01). The findings of this study highlight the importance of duration of differential activations for local fatigue, and support the hypothesis that long term differential activations prevent fatigue during prolonged low-level contractions.