FERI logo
  Logo UM
iMOVE
Extraction of information on muscle control during movements
7FP logo  
EU logo  

Research methodology

The proposed project represents one of the first attempts to decompose surface EMG during dynamic muscle contractions. It integrates the latest findings in the areas of isometric surface EMG decomposition, compound signal analysis, modelling of surface EMG, electronics, robotics with haptic interfaces and neurophysiology.

Surface EMG are being acquired by flexible 2D adhesive arrays of electrodes, designed in Laboratory of Engineering of Neuromuscular System and Motor Rehabilitation (LISiN) at Politecnico di Torino, Italy. Implementation modalities and performance of these electrode arrays have been progressively improved by exploiting flexible printed circuit technology, screen printing on thin plastic supports, embedding electrodes in flexible silicon rubber supports or in clothing (Fig. 1). With respect to dry electrode systems, these electrodes employ conductive gel, have a skin-electrode contact more stable over time and present smaller movement artefacts, even during fast dynamic contractions.

Picture of arrays
Fig. 1. Examples of surface electrode arrays: Flexible printed circuit with 5x6 electrodes (upper-left photo),  array of electrodes screen printed on mylar, applied with a double adhesive foam on a triceps (upper-central photo) and biceps brachii muscle (upper-right photo) and array of silver coated eyelets on cloth (lower-left photo). Conductive gel is injected into the eyelets. (Courtesy of LISiN laboratory, Politecnico di Torino.)

Despite the recent considerable literature concerning the development and the application of electrode arrays, the detection of surface EMG, both in ergonomics and in rehabilitation, is still currently based almost exclusively on single electrode pairs that provide a very limited local information strongly dependent on the location of the electrodes. This leads to poor repeatability of measurements. In addition, loss of contact has major consequences because of lack of redundancy. In this project, 2D grids of electrodes are being used, performing a sampling of the muscular electrical activity over a large surface area (Fig. 2). Namely, the analysis of individual motor unit properties from surface EMG requires the identification and classification of action potentials significantly contributing to the signal. This task is possible only if the motor units are uniquely represented by their surface action potentials. Contrary to intramuscular recordings, the number of motor units with surface action potentials significantly different from each other is very small when the action potentials are recorded with only a few electrodes (Fig. 2). However, the number of identifiable motor units increases substantially with the number of channels used for the discrimination. For example, in study by Farina et al. (Detecting the unique representation of motor-unit action potentials in the surface electromyogram. J Neurophysiol, 2008), more than 80% of experimentally detected motor units had unique surface representation when systems of 5×5 electrodes, spaced by 3-mm distance, were used to record isometric surface EMG from the abductor digiti minimi muscle. In the same study, one monopolar or bipolar recording allowed the discrimination of less than 5% of the motor units. In addition, preliminary results from the European project “Cybernetic Manufacturing Systems (CyberManS)” show that different workers performing the same task use their muscles in different ways. Since there is large inter-individual variability, it is clear that an EMG acquisition system must cover the entire muscle(s) of interest and adapt to their particularities. Thus, in general conditions, concurrent recordings from several locations over the skin surface are required. This requirement is even stronger in dynamic conditions, with muscle tissue moving relatively to the skin surface and, consequently, relatively to the acquisition system.

Figure 2
Fig. 2. Acquisition of surface and intramuscular EMG: The high selectivity of intramuscular electrodes enables the acquisition of high-fidelity signals with contributions from a limited number of motor units. Surface electrodes are located at a much larger distance from muscle fibers and exhibit much lower selectivity than intramuscular electrodes. This attenuates morphological differences between motor unit action potentials of different motor units. Thus, increased spatial support (i.e. the number of electrodes) of acquired surface EMG is required to reliably discriminate different motor units.

Aforementioned 2D acquisition modality presents at least two major problems. Firstly, the application of multi-channel detection systems over the skin surface is more time consuming than the use of classic bipolar electrode systems. This can be partially solved by disposable and pre-gelled systems which simplify the mounting procedures. Secondly, large number of acquired channels calls for advanced information extraction techniques, being capable of processing large quantities of data. In this project, information extraction techniques are being developed by integrating the most promising features of state-of-the-art blind source separation, in particular those based on time-frequency analysis, independent component analysis, sparse component analysis and especially on the novel convolution kernel compensation (CKC) technique. The latter was developed and validated within the applicant’s Marie Curie EIF project DEMUSE. CKC is fully automatic and nonparametric, implicitly resolves superimpositions of motor unit action potentials, and relies minimally on anatomic properties of the investigated muscle. Reconstructed motor unit discharge patterns are automatically tested against the predefined ranges of physiological variables (i.e., discharge rate, variability of inter-pulse interval, muscle fiber conduction velocity, etc.) and sorted with respect to the estimated degree of decomposition reliability. The method has been tested in a variety of isometric conditions, including simulated and experimental signals at constant and variable force levels. In all these tests, the CKC decomposition identified complete discharge patterns of up to 25 concurrently active motor units, more than any other existing surface EMG decomposition method.

Dynamic contractions

Due to complexity of acquired EMG, existing information extraction techniques have mainly been applied to the isometric muscle contractions, with the muscle geometry kept constant during the measurement session. However, the contractions of human muscles are almost always dynamic, with the muscle moving with respect to the skin. During the dynamic muscle contractions, the distances between the detection system and the active motor units change continuously as a function of time (Fig. 3). This causes continuous, but substantial changes in the shape of detected motor unit action potentials and hinders the extraction of information on individual motor units.

The abovementioned technical difficulties associated with motor unit recordings in humans limit the accuracy with which the muscle control strategies can be established during the movements. A common way to study the dynamic movements is to record the electrical activity at the surface of the skin above the investigated muscle, estimate its amplitude, and use it as an indicator of motor unit activity. However, the change in the surface EMG should not automatically be attributed to changes in either motor unit recruitment or motor unit discharge rate as the amplitude of surface EMG is farther influenced by the spatial distribution of motor units within the muscle, muscle movement with respect to the pick-up electrodes, degree of motor unit discharge synchronization and fatigue. Therefore, robust, accurate and reliable extraction of characteristics of individual motor units out of their composite surface EMG signals is required.

The CKC algorithm also exhibits a very low computational complexity and the studies of its real-time implementation are already underway at University of Maribor. Moreover, over the past 18 months, two different upgrades, namely cyclostationary CKC and sequential CKC, which allow constant adaptations of the existing method to the moderate changes in shapes of motor unit action potentials have been designed.  These methods are still under strict experimental validation but already demonstrate great potential for a decomposition of dynamic surface EMG recorded during moderate muscle movements.

Figure 2
Fig. 3. Motor unit action potentials (MUAPs) of a single motor unit as detected by the central column of the electrode array, shown in the upper-right photo of Fig. 1, from biceps brachii muscle during its slow dynamic contraction.  MUAPs (red lines) were obtained by spike-triggered averaging of surface EMG at different ranges of elbow joint angle γ (0° corresponds to full elbow extension). For comparison, the MUAP shapes at elbow joint angle between 10° and 30° are shown in blue. A shift of motor unit innervation zone is clearly visible.

Figure 3
Fig. 4. Smoothed discharge rates of different motor units identified by cyclostationary CKC algorithm from biceps brachii muscle during its slow dynamical contraction in dependence of elbow joint angle γ (0° corresponds to full elbow extension). Observe different firing patterns in concentric and eccentric phases of contraction.
© SSL, Faculty of Electrical Engineering and Computer Science, University of Maribor
Smetanova 17, SI-2000 Maribor, Slovenia