Physically demanding activities such as running cause both muscle development and strain. They also result in dynamic contractions which are defined as the shift in joint angles caused by the force from muscles (Hussain, et al., 2017). One of the most common methods utilized to study this phenomenon is electromyography (EMG). Despite the familiarity of EMG in myology, there is not enough literature to support the reliability and accuracy of this measure (Technology Review, 1999).
Not only that, numerous studies have highlighted the limitations of this method, which are spatial variability, cross talk, and the inability to test range of motion, muscle coordination, and activation (Hussain, et al., 2017; Technology Review, 1999; Hug, 2011; Dario, 2006; Campanini, 2007). Considering these concerns, EMG is not suggested to be the best option for assessment during dynamic contractions.
EMG has been found to be easily affected by methodological variance. For instance, in the study of EMG activity while running, several inconsistencies due to the lack of a standard methodology in measuring the muscular activities have been found (Subbu, Weiler, & Whyte, 2015). There is, therefore, a lack of a base measure to rely on. As a result, there is an interference in the collection of accurate data.
Looking closely at surface electromyography (sEMG), some of the causes of inconsistent data can be rooted. The non-invasive nature of this method is attractive, but this needs to be conducted with much caution as it poses the issue of crosstalk. According to Hug (2011), this is the leading cause of data disparity in EMG results as it involves “the contamination of the EMG signal by a nearby muscle’s electrical activity.” It is often found in small and proximal muscles such as those in the forearms (Hussain et al., 2017). It, thus, raises the question of how to isolate the targeted data.
In connection to crosstalk, there is the issue of spatial variability. Campanini et al. (2007) demonstrate the variance caused by the slight change of electrode placement. Indeed, it is difficult to consistently identify the accurate position of the electrode in different people. Dario (2006) states the challenge in the “heterogeneity of muscle fiber distribution.” Hug (2011) furthers by saying that it is likely to find disparity in the measure of muscular activity. Further research is, indeed, necessary to provide a more accurate data that can establish the reliability of EMG.
As aforementioned, therapeutic concerns are core to the use of EMG. Rehabilitation includes the study of muscle coordination which is “a distribution of muscle activation or force among individual muscles to produce a given combination of joint moments” (Hussain et al., 2017). In order to be a reliable source of data it is expected from EMG that it can provide a comprehensive measure of relevant areas including the range of motion (ROM). Unfortunately, EMG is also unable to detect ROM (Hussain et al., 2017). Seeing from these inadequacies, the need for an alternative measure is apparent.
It is almost hard to believe that with the advancements in today’s society, there is still insufficiencies in fields that concern everyday living. People’s kinesthetics highly depend on muscular strength. While it can be said that myology is more of an athletic concern, taking care of oneself is for everyone. Therefore, a need for a reliable assessment is imperative. EMG, despite its popularity, poses numerous limitations that are crucial to the understanding of the muscles. Because of this issue, EMG can be perceived as more of supplemental measure rather than as the primary mean of sourcing data.
Campanini, I., Merlo, A., Degola, P., Merletti, R., Vezzosi, G., & Farina, D. (2007). Effect of Electrode Location on EMG Signal Envelope in Leg Muscles During Gait. Journal of Electromyography and Kinesiology, 17, pp. 515-526.
Chapman, A.R., Vicenzino, B., Blanch, P., Knox, J.J., & Hodges, P.W. (2007). Intramascular fine-wire electromyography during cycling: Repeatability, normalization and a comparison to surface electromyography. Journal of Electromyography and Kinesiology, 20, pp. 108-117.
Dario, F. (2006). Interpretation of the Surface Electromyogram in Dynamic Contractions. Exercise and Sport Sciences Reviews, 34(3), pp. 121-127.
Hug, F. (2011). Can Muscle Coordination be Precisely Studied by Surface Electromyography?. Journal of Electromyography and Kinesiology, 21(1), pp. 1-12.
Hussain, J., Sundaraj, K., Low, Y.F., Kiang, L.C., Talib, I., & Nabi, F.G. (2017). Fatigue Assessment in the Brachii Muscles During Dynamic Contractions. International Journal of Applied Engineering Research, 12(22), pp. 12403-12408.
Subbu, R., Weiler, R., & Whyte, G. (2015). The Practical Use of Surface Electromyography During Running: Does the Evidence Support the Hype? A Narrative Review. BMJ Open Sport Exercise Medicine 2015, doi: 10.1136/ bmjsem-2015-000026
Technology Review: Dynamic Electromyography in Gait and Motion Analysis. (1999). AAEM Technology Reviews, 22(8), pp. 233-238.