Training a multivariable myoelectric mapping function to estimate fatigue
Author:
Publisher
Elsevier BV
Subject
Neurology (clinical),Biophysics,Neuroscience (miscellaneous)
Reference16 articles.
1. Simulation analysis of the performance of a novel high sensitive index for quantifying M-wave spectral changes during fatigue;Arabadzhiev;J Electromyogr Kinesiol,2005
2. Time–frequency parameters of the surface myoelectric signal for assessing muscle fatigue during cyclic dynamic contractions;Bonato;IEEE Trans Biomed Eng,2001
3. Changes in the surface EMG signal and the biomechanics of motion during a repetitive lifting task;Bonato;IEEE Trans Neural Syst Rehabil Eng,2002
4. Myoelectrical manifestations of localized muscular fatigue in humans;De Luca;CRC Crit Rev Biomed Eng,1984
5. Muscle fatigue during dynamic contractions assessed by new spectral indices;Dimitrov;Med Sci Sports Exerc,2006
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2. Using a System-Based Monitoring Paradigm to Assess Fatigue during Submaximal Static Exercise of the Elbow Extensor Muscles;Sensors;2021-02-03
3. Using EMG Amplitude and Frequency to Calculate a Multimuscle Fatigue Score and Evaluate Global Shoulder Fatigue;Human Factors: The Journal of the Human Factors and Ergonomics Society;2018-08-24
4. Single-Channel Techniques for Information Extraction from the Surface EMG Signal;Surface Electromyography : Physiology, Engineering, and Applications;2016-04-15
5. A comparison of EMG-based muscle fatigue assessments during dynamic contractions;Journal of Electromyography and Kinesiology;2013-10
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