Real time monitoring of muscular fatigue from dynamic surface myoelectric signals using a complex covariance approach

A method aimed at the real-time monitoring of muscular fatigue was implemented and optimized. The method is based on an estimate of the complex covariance function in order to evaluate, in real time, the mean frequency of the myoelectric signal spectrum. Real-time implementation is guaranteed by a r...

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Bibliographic Details
Published in:Medical engineering & physics 1999-05, Vol.21 (4), p.225-234
Main Authors: Conforto, S., D'Alessio, T.
Format: Article
Language:English
Subjects:
Adult
Algorithms
Analysis of Variance
Bioelectric phenomena
Biological and medical sciences
Complex covariance
Computational complexity
Dynamic myoelectric signal
Electrophysiology
Fatigue index
Fourier Analysis
Fourier transforms
Fundamental and applied biological sciences. Psychology
Humans
Investigative techniques, diagnostic techniques (general aspects)
Male
Medical sciences
Monitoring, Physiologic - methods
Monitoring, Physiologic - statistics & numerical data
Muscle
Muscle Fatigue - physiology
Normal Distribution
Osteoarticular system. Muscles
Pathology. Cytology. Biochemistry. Spectrometry. Miscellaneous investigative techniques
Random processes
Reference Values
Reproducibility of Results
Surface Properties
Time Factors
Vertebrates: body movement. Posture. Locomotion. Flight. Swimming. Physical exercise. Rest. Sports
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Summary:A method aimed at the real-time monitoring of muscular fatigue was implemented and optimized. The method is based on an estimate of the complex covariance function in order to evaluate, in real time, the mean frequency of the myoelectric signal spectrum. Real-time implementation is guaranteed by a recursive computation of the complex covariance and then of the mean frequency. The results show good performance on both synthetic and experimental non-stationary myoelectric signals recorded during fatiguing dynamic protocols. Performance in the presence of noise is highly satisfactory on both deterministic signals and stochastic processes, even when there are strong non-stationarities. Moreover, the computational complexity is highly reduced with respect to that offered by traditional methods based on short time Fourier transform.
ISSN:1350-4533
1873-4030
DOI:10.1016/S1350-4533(99)00049-1