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Electromyography Signal Acquisition and Processing for Movement Analysis
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This reprint focuses on recent advances in the processing of surface electromyography (EMG) signals acquired during human movement, as well as on innovative approaches to sense muscle activity. A wide range of methods is examined, including machine learning techniques to detect the onset/offset timing of muscle activity and approaches to evaluate muscle fatigue and analyze muscle synergies and co-contractions. Applications of these techniques are explored in different medical scenarios, e.g., for the benefit of patients suffering from low back pain, stroke survivors, and patients requiring polysomnography.
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Keywords
- autoregressive model
- Biochemical engineering
- Biotechnology
- Burg method
- co-contraction detection
- convolutional neural network
- dynamometer
- Electromyography
- EMG
- factor analysis
- fatiguing frequency-dependent lifting
- force estimation
- Gait
- gait analysis
- hand gesture recognition
- human-machine interface
- ipsilesional arm
- isometric contractions
- Locomotion
- Low back pain
- Machine learning
- mechanomyography
- MFRT
- motor module
- movement analysis
- MRC
- muscle activation
- muscle activation patterns
- muscle synergies
- muscular synergies
- neural networks
- Neurorehabilitation
- number of synergies
- onset detection
- Parkinsonism
- parkinson’s disease
- piezoelectric sensor
- polysomnography
- power spectral density
- prosthetic control
- RBD
- REM sleep behavior disorder
- REM sleep without atonia
- sEMG
- sEMG processing
- sitting balance
- spectral estimation techniques
- spectral power
- strength
- Stroke
- surface EMG
- surface EMG signal
- Technology, engineering, agriculture
- Technology: general issues
- the time–frequency domain
- trunk control
- trunk muscle coactivation
- VAF
- vibration sensor
- wavelet transform
- Welch method
Links
DOI: 10.3390/books978-3-0365-7205-5Editions