EMG amplitude, fatigue threshold, and time to task failure: A meta-analysis

Use of a factor analysis to assess biomechanical factors of American Sign Language in native and non-native signers

Prior studies suggest that native (born to at least one deaf or signing parent) and non-native signers have different musculoskeletal health outcomes from signing, but the individual and combined biomechanical factors driving these differences are not fully understood. Such group differences in signing may be explained by the five biomechanical factors of American Sign Language that have been previously identified: ballistic signing, hand and wrist deviations, work envelope, muscle tension, and "micro" rests. Prior work used motion capture and surface electromyography to collect joint kinematics and muscle activations, respectively, from ten native and thirteen non-native signers as they signed for 7.5 min. Each factor was individually compared between groups. A factor analysis was used to determine the relative contributions of each biomechanical factor between signing groups. No significant differences were found between groups for ballistic signing, hand and wrist deviations, work envelope volume, excursions from recommended work envelope, muscle tension, or "micro" rests. Factor analysis revealed that "micro" rests had the strongest contribution for both groups, while hand and wrist deviations had the weakest contribution. Muscle tension and work envelope had stronger contributions for native compared to non-native signers, while ballistic signing had a stronger contribution for non-native compared to native signers. Using a factor analysis enabled discernment of relative contributions of biomechanical variables across native and non-native signers that could not be detected through isolated analysis of individual measures. Differences in the contributions of these factors may help explain the differences in signing across native and non-native signers.

Rate of change in longitudinal EMG indicates time course of an individual's neuromuscular adaptation in resistance-based muscle training

An individual's long-term neuromuscular adaptation can be measured through time-domain analyses of surface electromyograms (EMG) in regular resistance-based training. The perceived changes in recruitment, such as those measured during muscle fatigue, can subsequently prolong the recovery time in rehabilitation applications. Thus, by developing quantifiable methods for measuring neuromuscular adaptation, adjuvant treatments applied during neurorehabilitation can be improved to reduce recovery times and to increase patient quality of care. This study demonstrates a novel time-domain analysis of long-term changes in EMG captured neuromuscular activity that we aim to use to develop a quantified performance metric for muscle-based intervention training and optimization of an individual. We measure EMG of endurance and hypertrophy-based resistance exercises of healthy participants over 100 days to identify trends in long-term neuromuscular adaptation. Particularly, we show that the rate of EMG amplitude increase (motor recruitment) is dependent on the training modality of an individual. Particularly, EMG decreases over time with repetitive training – but the rate of decrease is different in hypertrophy, endurance, and control exercises. We found that the EMG peak contraction decreases across all subjects, on average, by 8.23 dB during hypertrophy exercise and 10.09 dB for endurance exercises over 100 days of training, while control participants showed negligible change. This represents approximately 2 dB difference EMG activity when comparing endurance and hypertrophy exercises, and >8 dB change when comparing to our control cases. As such, we show that the slope of the long-term EMG activity is related to the resistance-based exercise. We believe this can be used to identify person-specific performance metrics, and to create optimized interventions using a measured performance baseline of an individual.

Power-assisted Pedicle Screw Technique Protects Against Risk of Surgeon Overuse Injury: A Comparative Electromyography Study of the Neck and Upper Extremity Muscle Groups in a Simulated Surgical Environment

STUDY DESIGN

Cadaveric.

OBJECTIVE

The aim of this study was to quantify the amplitude and duration of surgeons' muscle exertion from pedicle cannulation to screw placement using both manual and power-assisted tools in a simulated surgical environment using surface electromyography (EMG).

SUMMARY OF BACKGROUND DATA

A survey of Scoliosis Research Society members reported rates of neck pain, rotator cuff disease, lateral epicondylitis, and cervical radiculopathy at 3 ×, 5 ×, 10 ×, and 100 × greater than the general population. The use of power-assisted tools in spine surgery to facilitate pedicle cannulation through screw placement during open posterior fixation surgery may reduce torque on the upper limb and risk of overuse injury.

METHODS

Pedicle preparation and screw placement was performed from T4-L5 in four cadavers by two board-certified spine surgeons using both manual and power-assisted techniques. EMG recorded muscle activity from the flexor carpi radialis, extensor carpi radialis, biceps, triceps, deltoid, upper trapezius, and neck extensors. Muscle activity was reported as a percentage of the maximum voluntary exertion of each muscle group (%MVE) and muscle exertion was linked to low- (0-20% MVE), moderate- (20%-45% MVE), high- (45%-70% MVE) and highest- (70%-100% MVE) risk of overuse injury based on literature.

RESULTS

Use of power-assisted tools for pedicle cannulation through screw placement maintains average muscle exertion at low risk for overuse injury for every muscle group. Conversely with manual technique, the extensor carpi radialis, biceps, upper trapezius and neck extensors operate at levels of exertion that risk overuse injury for 50% to 92% of procedure time. Powerassisted tools reduce average muscle exertion of the biceps, triceps, and deltoid by upwards of 80%.

CONCLUSION

Power-assisted technique protects against risk of overuse injury. Elevated muscle exertion of the extensor carpi radialis, biceps, upper trapezius, and neck extensors during manual technique directly correlate with surgeons' self-reported diagnoses of lateral epicondylitis, rotator cuff disease, and cervical myelopathy.Level of Evidence: N/A.

Non-obstructive monitoring of muscle fatigue for low intensity dynamic exercise with infrared thermography technique

Surface electromyography (sEMG) has been widely used in evaluating muscle fatigue among athletes where electrodes are attached on the skin during the activity. Recently, infrared thermography technique (IRT) has gain popularity and shown to be another preferred method in monitoring and predicting muscle fatigue non-obstructively. This paper investigates the correlation between surface temperature and muscle activation parameters obtained using both IRT and sEMG methods simultaneously. Twenty healthy subjects were required to perform a repetitive calf raise exercise with various loads attached around their ankle for 3 min to induce fatigue on the targeted gastrocnemius muscles. Average temperature and temperature difference information were extracted from thermal images, while root mean square (RMS) and median frequency (MF) were extracted from sEMG signals. Spearman statistical analysis performed shows that there is a significant correlation between average temperature with RMS and between temperature difference with MF values at p<0.05. While ANOVA test conducted shows that there is significant impact of loads on RMS and MF where F=12.61 and 3.59, respectively, at p< 0.05. This study suggested that skin surface temperature can be utilized in monitoring and predicting muscle fatigue in low intensity dynamic exercise and can be extended to other dynamic exercises.

Evaluation of High-Intensity Interval Training and Beta-Alanine Supplementation on Efficiency of Electrical Activity and Electromyographic Fatigue Threshold

ABSTRACT

Herda, AA, Smith-Ryan, AE, Kendall, KL, Cramer, JT, and Stout, JR. Evaluation of high-intensity interval training and beta-alanine supplementation on efficiency of electrical activity and electromyographic fatigue threshold. J Strength Cond Res 35(6): 1535-1541, 2021-The purpose of this study was to determine the effects of high-intensity interval training (HIIT) with or without β-alanine (BA) supplementation on the electromyographic fatigue threshold (EMGFT) and efficiency of electrical activity (EEA) in young women. Forty-four women (mean ± SD; age [yrs]: 21.7 ± 3.7; height [cm]: 166.3 ± 6.4; body mass [kg]: 66.1 ± 10.3) were randomly assigned to one of 3 treatment groups. The supplement groups performed HIIT on the cycle ergometer 3 times·wk-1 for 6 weeks. Electromyographic fatigue threshold and EEA were assessed at baseline (PRE), after 3 weeks of training (MID), and after 6 weeks of HIIT (POST). Two 2-way mixed factorial analyses of variance (time [PRE vs. MID vs. POST] × treatment (BA vs. PL vs. CON)] were used to analyze EMGFT and EEA with a predetermined level of significance α of 0.05. For EMGFT, there was no interaction (p = 0.26) and no main effect for time (p = 0.28) nor treatment (p = 0.86); thus, there were no changes in EMGFT regardless of training or supplementation status. For EEA, there was no interaction (p = 0.70) nor treatment (p = 0.79); however, there was a main effect for time (p < 0.01). Our findings indicated that neither training nor supplementation was effective in improving EMGFT in women. Efficiency of electrical activity was altered, potentially because of a learning effect. Coaches and practitioners may not use these tests to monitor training status; however, they may find EEA as a useful tool to track cycling efficiency.

Exhausting repetitive piano tasks lead to local forearm manifestation of muscle fatigue and negatively affect musical parameters

Muscle fatigue is considered as a risk factor for developing playing-related muscular disorders among professional pianists and could affect musical performance. This study investigated in 50 pianists the effect of fatiguing repetitive piano sequences on the development of forearm muscle fatigue and on piano performance parameters. Results showed signs of myoelectric manifestation of fatigue in the 42-electromyographic bipolar electrodes positioned on the forearm to record finger and wrist flexor and extensor muscles, through a significant non-constant decrease of instantaneous median frequency during two repetitive Digital (right-hand 16-tones sequence) and Chord (right-hand chords sequence) excerpts, with extensor muscles showing greater signs of fatigue than flexor muscles. In addition, muscle fatigue negatively affected key velocity, a central feature of piano sound intensity, in both Digital and Chord excerpts, and note-events, a fundamental aspect of musicians' performance parameter, in the Chord excerpt only. This result highlights that muscle fatigue may alter differently pianists' musical performance according to the characteristics of the piece played.

Wheelchair propulsion fatigue thresholds in electromyographic and ventilatory testing

STUDY DESIGN

Qualitative study.

OBJECTIVE

The objective of the present study are physiological processes occurring when the intensity of manual wheelchair propulsion approaches levels causing muscular fatigue. In particular, we set out to (1) detect the electromyographic (EMG) and ventilatory fatigue threshold during a single wheelchair incremental test, (2) examine the relationship between EMG threshold (EMGT) and ventilatory threshold (VT), and (3) detect the EMG threshold differences between the propulsive and recovery muscle synergies.

SETTING

Biomechanics laboratory at the University of Alberta, Canada.

METHODS

Oxygen uptake and EMG signals from ten wheelchair users (seven males and three females) were recorded as they were each performing an incremental propulsion bout in their own wheelchairs on a wheelchair ergometer. The V-slope method was used to identify the VT, and the EMGT of each of the eight muscles (anterior deltoid, middle deltoid, posterior deltoid, infraspinatus, upper trapezius, sternal head of pectoralis major, biceps brachii, and triceps brachii) was determined using the bisegmental linear regression method.

RESULTS

For each participant, we were able to determine the EMGT and VT from a single incremental wheelchair propulsion bout. EMGT stands in good agreement with VT, and there was a high similarity in EMGT between push and recovery muscles (intraclass correlation coefficient = 0.91).

CONCLUSION

The EMG fatigue threshold method can serve as a valid and reliable tool for identifying the onset of muscular fatigue during wheelchair propulsion, thus providing a foundation for automated muscle fatigue detection/prediction in wearable technology.

Muscle endurance time estimation during isometric training using electromyogram and supervised learning

Constant-force isometric muscle training is useful for increasing the maximal strength , rehabilitation and work-fatigue assessment. Earlier studies have shown that muscle fatigue characteristics can be used for evaluating muscle endurance limit.

STUDY OBJECTIVE

To predict muscle endurance time during isometric task using frequency spectrum characteristics of surface electromyography signals along with analysis of frequency spectrum shape and scale during fatigue accumulation.

METHOD

Thirteen subjects performed isometric lateral raise at 60% MVC of deltoid (lateral) till endurance limit. Time windowed sEMG frequency spectrum was modelled using 2-parameter distributions namely Gamma and Weibull for spectrum analysis and endurance prediction.

RESULTS

Gamma distribution provided better spectrum fitting (P < 0.001) than Weibull distribution. Spectrum Distribution demonstrated no change in shape but shifted towards lower frequency with increase of magnitude at characteristic mode frequency. Support Vector Regression based algorithm was developed for endurance time estimation using features derived from fitted frequency spectrum. Time taken till endurance limit for acquired dataset 38.53 ± 17.33 s (Mean ± Standard Deviation) was predicted with error of 0.029 ± 4.19 s . R-square: 0.956, training and test sets RMSE was calculated as 3.96 and 4.29 s respectively. The application of the algorithm suggested that model required 70% of sEMG signal from maximum time of endurance for high prediction accuracy.

CONCLUSION

Endurance Limit prediction algorithm was developed for quantification of endurance time for optimizing isometric training and rehabilitation. Our method could help personalize and change conventional training method of same weight and duration for all subjects with optimized training parameters, based upon individual sEMG activity.