In this study, we aimed to determine if electromyography (EMG) normalization to maximal voluntary isometric contractions (MVIC) was influenced by subacromial pain in patients with subacromial impingement syndrome. Patients performed MVICs in unique testing positions for each shoulder muscle tested before and after subacromial injection of local anesthetic. In addition to collection of MVIC data, EMG data during an arm elevation task were recorded before and after injection. From a visual analog pain scale, patients had a 64% decrease in pain following the injection. Significant increases in MVICs were noted in 4 of the 7 shoulder muscles tested: anterior, middle and posterior deltoid, and lower trapezius. No significant differences were noticed for the upper trapezius, latissimus dorsi, or serratus anterior. MVIC condition (pre and post injection) had a significant influence on EMG normalization for the anterior deltoid and lower trapezius muscle. Results indicate that subacromial pain can influence shoulder muscle activity, especially for the deltoid muscles and lower trapezius. In addition, normalization to MVIC in the presence of pain can have unpredictable results. Caution should be taken when normalizing EMG data to MVIC in the presence of pain.
Lucas Ettinger, Jason Weiss, Matthew Shapiro and Andrew Karduna
Mark A. Sutherlin, L. Colby Mangum, Jay Hertel, Susan A. Saliba and Joseph M. Hart
Key Points ▸ Correlations exist between anthropometric measures and transversus abdominis and lumbar multifidus muscle thickness, but are influenced by positions and history of low back pain status. ▸ Mass and body mass index were the most consistent normalization variables for the transversus
Remco J. Baggen, Jaap H. van Dieën, Sabine M. Verschueren, Evelien Van Roie and Christophe Delecluse
placement, skin preparation, and impedance of the skin interface and tissue layer between electrodes and muscle. 1 , 3 To allow for comparisons of muscle activation between participants and within participants between different measurement sessions, EMG signals need to be normalized. 4 Normalization is
David R. Mullineaux, Clare E. Milner, Irene S. Davis and Joseph Hamill
The appropriateness of normalizing data, as one method to reduce the effects of a covariate on a dependent variable, should be evaluated. Using ratio, 0.67-nonlinear, and fitted normalizations, the aim of this study was to investigate the relationship between ground reaction force variables and body mass (BM). Ground reaction forces were recorded for 40 female subjects running at 3.7 ± 0.18 m·s–1 (mass = 58 ± 6 kg). The explained variance for mass to forces (peak-impact-vertical = 70%; propulsive-vertical = 27%; braking = 40%) was reduced to < 0.1% for mass to ratio normalized forces (i.e., forces/BM1) with statistically significantly different power exponents (p < 0.05). The smaller covariate effect of mass on loading rate variables of 2–16% was better removed through fitted normalization (e.g., vertical-instantaneous-loading-rate/BM0.69±0.93; ±95% CI) with nonlinear power exponents ranging from 0.51 to 1.13. Generally, these were similar to 0.67 as predicted through dimensionality theory, but, owing to the large confidence intervals, these power exponents were not statistically significantly different from absolute or ratio normalized data (p > 0.05). Further work is warranted to identify the appropriate method to normalize loading rates either to mass or to another covariate. Ratio normalization of forces to mass, as predicted through Newtonian mechanics, is recommended for comparing subjects of different masses.
Stephen M. Suydam, Kurt Manal and Thomas S. Buchanan
tool for analyzing muscle activation across muscles, tasks, subjects, and testing sessions requires EMG signals to be normalized to a reference value. 8 The need for normalization becomes even greater between days if electrodes are removed and replaced without guide markings to ensure precise
Mallory Mann and Vikki Krane
to perform gender and sex in a myriad of ways, cultural expectations and social rewards encourage hegemonic representations which then normalize the heterosexual matrix. As Waldron ( 2016 ) expressed, “despite the fluidity of gender and sexuality through performative acts, repeated performances of
Stephen M. Glass, Brian L. Cone, Christopher K. Rhea, Donna M. Duffy and Scott E. Ross
anthropometric dimorphism between males and females. Other factors aside, it is reasonable to expect that a proportionally larger individual will sway more in absolute terms than a relatively small individual. Accordingly, previous work has made efforts to normalize anthropometric trends prior to studying sex
Ferdous Wahid, Rezaul Begg, Noel Lythgo, Chris J. Hass, Saman Halgamuge and David C. Ackland
Normalization of gait data is performed to reduce the effects of intersubject variations due to physical characteristics. This study reports a multiple regression normalization approach for spatiotemporal gait data that takes into account intersubject variations in self-selected walking speed and physical properties including age, height, body mass, and sex. Spatiotemporal gait data including stride length, cadence, stance time, double support time, and stride time were obtained from healthy subjects including 782 children, 71 adults, 29 elderly subjects, and 28 elderly Parkinson’s disease (PD) patients. Data were normalized using standard dimensionless equations, a detrending method, and a multiple regression approach. After normalization using dimensionless equations and the detrending method, weak to moderate correlations between walking speed, physical properties, and spatiotemporal gait features were observed (0.01 < |r| < 0.88), whereas normalization using the multiple regression method reduced these correlations to weak values (|r| < 0.29). Data normalization using dimensionless equations and detrending resulted in significant differences in stride length and double support time of PD patients; however the multiple regression approach revealed significant differences in these features as well as in cadence, stance time, and stride time. The proposed multiple regression normalization may be useful in machine learning, gait classification, and clinical evaluation of pathological gait patterns.
Nick Ball and Joanna Scurr
Electromyograms used to assess neuromuscular demand during high-velocity tasks require normalization to aid interpretation. This paper posits that, to date, methodological approaches to normalization have been ineffective and have limited the application of electromyography (EMG). There is minimal investigation seeking alternative normalization methods, which must be corrected to improve EMG application in sports. It is recognized that differing normalization methods will prevent cross-study comparisons. Users of EMG should aim to identify normalization methods that provide good reliability and a representative measure of muscle activation. The shortcomings of current normalization methods in high-velocity muscle actions assessment are evident. Advances in assessing alternate normalization methods have been done in cycling and sprinting. It is advised that when normalizing high-intensity muscle actions, isometric methods are used with caution and a dynamic alternative, where the muscle action is similar to that of the task is preferred. It is recognized that optimal normalization methods may be muscle and task dependent.
John W. Wannop, Jay T. Worobets and Darren J. Stefanyshyn
Authors who report ground reaction force (GRF), free moment (FM), and resultant joint moments usually normalize these variables by division normalization. Normalization parameters include body weight (BW), body weight x height (BWH), and body weight x leg length (BWL). The purpose of this study was to explore the appropriateness of division normalization, power curve normalization, and offset normalization on peak GRF, FM, and resultant joint moments. Kinematic and kinetic data were collected on 98 subjects who walked at 1.2 and 1.8 m/s and ran at 3.4 and 4.0 m/s. Linear curves were best fit to the data, and regression analyses performed to test the significance of the correlations. It was found that the relationship between peak force and BW, as well as joint moments and BW, BWH, and BWL, were not always linear. After division normalization, significant correlations were still found. Power curve and offset normalization, however, were effective at normalizing all variables; therefore, when attempting to normalize GRF and joint moments, perhaps nonlinear or offset methods should be implemented.