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Lucas Ettinger, Jason Weiss, Matthew Shapiro, and Andrew Karduna

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.

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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

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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

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Steven M. Hirsch, Christopher J. Chapman, David M. Frost, and Tyson A.C. Beach

, characteristics like body mass and height may also explain a significant proportion of the variance in musculoskeletal tissue properties (eg, the cross-sectional area of the anterior cruciate ligament 2 ), which may make body mass or height normalized NJM magnitudes a reasonable proxy of the normalized loads (ie

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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.

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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

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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

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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

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Jefferson J. dos Santos, Rebeca O. Nagy, Leonardo Intelangelo, Isabella C. Ferreira, Michelle A. Barbosa, Gabriela Silveira-Nunes, and Alexandre Carvalho Barbosa

The normalization process is universally advocated and highly recommended by the International Society of Electromyography and Kinesiology as a form to rescale the electromyographic data (sEMG) to a percentage of a reference value. 1 , 2 A recent consensus was developed to summarize the

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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.