Analysis of the EMG Signal During Cyclic Movements Using Multicomponent AM-FM Decomposition

Sport, fitness, as well as rehabilitation activities, often require the accomplishment of repetitive movements. The correctness of the exercises is often related to the capability of maintaining the required cadence and muscular force. Failure to maintain the required force, also known as muscle fat...

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Bibliographic Details
Published inIEEE journal of biomedical and health informatics Vol. 19; no. 5; pp. 1672 - 1681
Main Authors Biagetti, Giorgio, Crippa, Paolo, Curzi, Alessandro, Orcioni, Simone, Turchetti, Claudio
Format Journal Article
LanguageEnglish
Published United States IEEE 01.09.2015
Subjects
Adult
Demodulation
Electromyography
Electromyography - methods
Fatigue
Frequency estimation
Humans
Informatics
Middle Aged
Muscle Fatigue - physiology
Muscle, Skeletal - physiology
Muscles
Signal Processing, Computer-Assisted
Time-frequency analysis
Hilbert transform
cadence estimation
surface EMG
muscle fatigue
cyclic movements
AM-FM
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Summary:Sport, fitness, as well as rehabilitation activities, often require the accomplishment of repetitive movements. The correctness of the exercises is often related to the capability of maintaining the required cadence and muscular force. Failure to maintain the required force, also known as muscle fatigue, is accompanied by a shift in the spectral content of the surface electromyography (EMG) signal toward lower frequencies. This paper presents a novel approach for simultaneously obtaining exercise repetition frequency and evaluating muscular fatigue, as functions of time, by only using the EMG signal. The mean frequency of the amplitude spectrum (MFA) of the EMG signal, considered as a function of time, is directly related to the dynamics of the movement performed and to the fatigue of the involved muscles. If the movement is cyclic, MFA will display the same pattern and its average will tend to decrease. These two effects have been simultaneously modeled by a two-component AM-FM model based on the Hilbert transform. The method was tested on signals recorded using a wireless system applied to healthy subjects performing dumbbell biceps curls, dumbbell lateral rises, and bodyweight squats. Experimental results show the excellent performance of the proposed technique.
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ISSN:2168-2194
2168-2208
2168-2208
DOI:10.1109/JBHI.2014.2356340