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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., (2009 Elsevier Ltd. All rights reserved.)

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.

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.

Motor unit (MU) synchronization is the result of commonality in the pre-synaptic input to MUs. Previously proposed techniques to estimate MU synchronization based on invasive and surface electromyography (sEMG) recordings have been, respectively, limited by the analyzed MU population size and influence of changes in muscle fibre conduction velocities (MFCVs). The aim of this paper was to evaluate a novel descriptor of MU synchronization on a large MU population, and to minimize its dependency on MFCV. The method is based on the asymmetry of MU action potentials, causing synchronized MU action potentials to skew the monopolar sEMG signal distribution. The descriptor was the skewness statistic used on sub-band filtered monopolar sEMG signals (sub-band skewness). The method was evaluated using simulated signals and its performance was evaluated in terms of bias and sensitivity of the sub-band skewness quantifying the MU synchronization level. The best sensitivity was obtained using sub-band filtering at scale 5 (Mexican hat wavelet). The sensitivity was in general about 0.1units per 5% MU synchronization level. Changes in MFCV had a minimal influence, and caused at most a 5% deviant MU synchronization quantification level. A halved recruitment level had higher bias and a 20% lower sensitivity. Increased firing rate (14-34Hz) reduced the sensitivity about 50%. The sensitivity of the descriptor was robust to noise, and different volume conduction properties. It should be noted that the sub-band skewness comprises a subject-dependent component implying that only changes in MU synchronization level can be quantified.

In the nearest future a lot of post-operative treatment and health monitoring is going to be performed in people's homes instead of in hospital. The increasing number of elderly people in the developed countries and the need for more advanced medical treatment and equipment in conjunction with economical demands will force the development of more cost-effective solutions. The equipment used must be very rugged and safe for both the patient and the operator and easily configured for a number of different measurement parameters. In most cases storing data on a memory card in the equipment will be sufficient but sometimes a wireless real-time measurement system is needed. This paper presents an acquisition system flexible enough for a number of different applications, home care, mobile health care and other biomedical measurement situations. A prototype system has been tested with sufficient performance for several physiological signals.

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.