Surface EMG in Clinical Assessment and Neurorehabilitation: Barriers Limiting Its Use

Abstract

This article addresses the potential clinical value of techniques based on surface electromyography (sEMG) in rehabilitation medicine with specific focus on neurorehabilitation. Applications in exercise and sport pathophysiology, in movement analysis, in ergonomics and occupational medicine, and in a number of related fields are also considered. The contrast between the extensive scientific literature in these fields and the limited clinical applications is discussed. The "barriers" between research findings and their application are very broad, and are longstanding, cultural, educational, and technical. Cultural barriers relate to the general acceptance and use of the concept of objective measurement in a clinical setting and its role in promoting Evidence Based Medicine. Wide differences between countries exist in appropriate training in the use of such quantitative measurements in general, and in electrical measurements in particular. These differences are manifest in training programs, in degrees granted, and in academic/research career opportunities. Educational barriers are related to the background in mathematics and physics for rehabilitation clinicians, leading to insufficient basic concepts of signal interpretation, as well as to the lack of a common language with rehabilitation engineers. Technical barriers are being overcome progressively, but progress is still impacted by the lack of user-friendly equipment, insufficient market demand, gadget-like devices, relatively high equipment price and a pervasive lack of interest by manufacturers. Despite the recommendations provided by the 20-year old EU project on "Surface EMG for Non-Invasive Assessment of Muscles (SENIAM)," real international standards are still missing and there is minimal international pressure for developing and applying such standards. The need for change in training and teaching is increasingly felt in the academic world, but is much less perceived in the health delivery system and clinical environments. The rapid technological progress in the fields of sensor and measurement technology (including sEMG), assistive devices, and robotic rehabilitation, has not been driven by clinical demands. Our assertion is that the most important and urgent interventions concern enhanced education, more effective technology transfer, and increased academic opportunities for physiotherapists, occupational therapists, and kinesiologists.

Keywords: clinical applications; education; motion analysis; movement sciences; physiotherapy; rehabilitation; sEMG; surface electromyography.

Figure 1

Rate of publication of sEMG articles on international peer-reviewed journals. These articles and more than 20 textbooks (see: https://www.robertomerletti.it/en/emg/material/books/) provide a huge body of knowledge that ranges from technical issues to clinical applications in research labs. A Pubmed search (June 2020, keywords “surface electromyography” OR sEMG) indicated over 5,500 publications (14.8% of the 37,000 publications listed in Pubmed under “neurorehabilitation”). Over 180 review papers are listed by Pubmed in the sEMG field. In most countries, this knowledge is not translated into routine applications for planning treatment, monitoring and assessing outcome in neurorehabilitation.

Figure 2

Example of the advances in sEMG detection in the last 70 years. (A) The detection system used by Floyd and Silver in 1950 (20) to monitor abdominal muscles. The electronics used for signal conditioning had the size of a suitcase. (B) Modern system for detection, condition, A/D conversion, and transmission of signals from sEMG electrode arrays. The white box contains the system described in (C) and the rechargeable battery to supply it for a few hours. (C) Schematic diagram of the signal detection, conditioning, conversion, and transmission depicted in (B). Two systems, with different detection grids of 32 electrodes each are applied to the rectus femoris and vastus medialis. Up to four such systems can operate simultaneously and provide images of sEMG activity in four locations (21). (A) Is reprinted, with permission from Floyd and Silver (20).

Figure 3

Torque at the elbow and average rectified value (ARV), estimated on epochs of 0.5 s, of the sEMG obtained from three pairs of electrodes placed between the IZ and the tendon endings of the long and short head of the biceps brachii (BBlh, BBsh) and of the brachioradialis (BR) of two healthy subjects (A and B). All values are expressed as percent of the initial value, which is defined here as the intercept of the linear regression of the experimental values (dashed lines). Results from two 5-s isometric constant torque contractions performed at 20% MVC and at 50% MVC are presented. A progressively changing load sharing among the three muscles is evident and different in the two subjects (A and B). Different conclusions would have been reached depending on which single muscle had been monitored (unpublished data).

Figure 4

Example of application of a 16 × 8 grid on the dorsal side of the forearm to identify/monitor the regions of activity of the finger extensors. The colors represent the intensity (RMS value) of the longitudinal differential signals (15 × 8 channels). Dark red = strong signal, dark blue = no signal. Interelectrode distance: 10 mm (unpublished data).

Figure 5

Averaged sEMG envelopes of the Biceps Brachii as a function of the movement velocity during freely performed elbow extension movements. Values are normalized with respect to the 75% of the maximal value of the envelope. The sEMG when the elbow passes an interval from 80 to 70° flexion angle is analyzed. In healthy volunteers (blue), the biceps sEMG envelope decreases with increasing angular velocity. In contrast, in the patient with a spastic movement disorder (red) shown here, muscular activation increases with angular velocity. The gradient of the sEMG envelope–movement velocity relationship is thus a measure for the presence of spasticity during freely performed movements (unpublished data).

Figure 6

Ankle kinematics and sEMG data from two stroke patients with equinus foot during the swing phase of gait. The same kinematics can be observed, with different underlying mechanisms. In both cases the activity of the tibialis anterior is present at foot off as required to lift the forefoot. On the left, the activity of the gastrocnemius lateralis during the swing phase hinders dorsiflexion. On the right, there is no activity of the triceps surae during the swing phase, and the lack of dorsiflexion is due to the triceps stiffness only. In this situation, sEMG is needed to support the decision-making concerning intervention. Unpublished data acquired in a research project approved by the local Ethics Committee (2017/0123710).

Figure 7

Effect of episiotomy on the EAS innervation pattern. The identified EAS motor units, indicated by red arcs, are not necessarily all the motor units of the EAS. (A,B) Identified MUs and their IZs, 4–6 weeks pre-delivery and 6 weeks post-delivery with right mediolateral episiotomy, in one subject. (C,D) Circular histograms of the number of EAS IZs pre- and post-delivery in 86 cases of right episiotomy (out of 331 deliveries). Both histograms are normalized with respect to the highest bin. The change in the RV quadrant of the EAS is statistically significant (123). V, ventral; L, left; D, dorsal; R, right. (A,B) Reproduced with permission from Cescon et al. (123), (C,D) Reproduced, with permission from Di Vella et al. (186).

Figure 8

Example of two electrode grids applied to the trapezius muscle to study its activity during typing on a keyboard with and without arm rest on the desk. Images are interpolated and show the sEMG RMS distribution in space (see movies at URL https://www.robertomerletti.it/en/emg/material/videos/f6/ and https://www.robertomerletti.it/en/emg/material/videos/f7/).