Motor control retraining exercises for shoulder impingement: effects on function, muscle activation, and biomechanics in young adults

Objective: Evidence for effective management of shoulder impingement is limited. The present study aimed to quantify the clinical, neurophysiological, and biomechanical effects of a scapular motor control retraining for young individuals with shoulder impingement signs.Method: Sixteen adults with shoulder impingement signs (mean age 22 ? 1.6 years) underwent the intervention and 16 healthy participants (24.8 ? 3.1years) provided reference data. Shoulder function and pain were assessed using the Shoulder Pain and Disability Index (SPADI) and other questionnaires. Electromyography (EMG) and 3 dimensional motion analysis was used to record muscle activation and kinematic data during arm elevation to 90? and lowering in 3 planes. Patients were assessed pre and post a 10-week motor control based intervention, utilizing scapular orientation retraining.Results: Pre-intervention, patients reported pain and reduced function compared to the healthy participants (SPADI in patients 20 ? 9.2; healthy 0 ? 0). Post intervention, the SPADI scores reduced significantly (P < .001) by a mean of 10 points (?4). EMG showed delayed onset and early termination of serratus anterior and lower trapezius muscle activity pre-intervention, which improved significantly post-intervention (P < .05). Pre intervention, patients exhibited on average 4.6-7.4? less posterior tilt, which was significantly lower in 2 arm elevation planes (P < .05) than healthy participants. Postintervention, upward rotation and posterior tilt increased significantly (P <.05) during 2 arm movements, approaching the healthy values.Conclusion: A 10-week motor control intervention for shoulder impingement increased function and reduced pain. Recovery mechanisms were indicated by changes in muscle recruitment andscapular kinematics. The efficacy of the intervention requires further examined in a randomizedcontrol trial

Complexity Analysis of Surface Electromyography for Assessing the Myoelectric Manifestation of Muscle Fatigue: A Review

The surface electromyography (sEMG) records the electrical activity of muscle fibers during contraction: one of its uses is to assess changes taking place within muscles in the course of a fatiguing contraction to provide insights into our understanding of muscle fatigue in training protocols and rehabilitation medicine. Until recently, these myoelectric manifestations of muscle fatigue (MMF) have been assessed essentially by linear sEMG analyses. However, sEMG shows a complex behavior, due to many concurrent factors. Therefore, in the last years, complexity-based methods have been tentatively applied to the sEMG signal to better individuate the MMF onset during sustained contractions. In this review, after describing concisely the traditional linear methods employed to assess MMF we present the complexity methods used for sEMG analysis based on an extensive literature search. We show that some of these indices, like those derived from recurrence plots, from entropy or fractal analysis, can detect MMF efficiently. However, we also show that more work remains to be done to compare the complexity indices in terms of reliability and sensibility; to optimize the choice of embedding dimension, time delay and threshold distance in reconstructing the phase space; and to elucidate the relationship between complexity estimators and the physiologic phenomena underlying the onset of MMF in exercising muscles

Overview of processing techniques for surface electromyography signals

Surface electromyography (sEMG) is a technology to assess muscle activation, which is an important component in applications related to diagnosis, treatment, progression assessment, and rehabilitation of specific individuals' conditions. Recently, sEMG potential has been shown, since it can be used in a non-invasive manner; nevertheless, it requires careful signal analysis to support health professionals reliably. This paper briefly described the basic concepts involved in the sEMG, such as the physiology of the muscles, the data acquisition, the signal processing techniques, and classification methods that may be used to identify disorders or signs of abnormalities according to muscular patterns. Specifically, classification methods encompass digital signal processing techniques and machine learning with high potential in the field. We hope that this work serves as an introduction to researchers interested in this field.Comment: 11 pages, 7 figure