Electromyography Signal Acquisition and Processing for Movement Analysis

• Autres auteurs: Di Nardo, Francesco (Éditeur intellectuel), Agostini, Valentina (Éditeur intellectuel), Conforto, Silvia (Éditeur intellectuel)
• Format: Électronique Chapitre de livre
• Langue: anglais
• Publié: Basel MDPI - Multidisciplinary Digital Publishing Institute 2023
• Sujets: Technology: general issues; Biotechnology; gait; locomotion; motor module; number of synergies; VAF; gait analysis; EMG; muscle activation patterns; movement analysis; muscle synergies; sEMG; stroke; factor analysis; neurorehabilitation; MRC; dynamometer; strength; mechanomyography; piezoelectric sensor; vibration sensor; human-machine interface; prosthetic control; hand gesture recognition; convolutional neural network; electromyography; polysomnography; REM sleep without atonia; REM sleep behavior disorder; RBD; parkinsonism; Parkinson's disease; spectral power; sitting balance; trunk control; ipsilesional arm; MFRT; fatiguing frequency-dependent lifting; low back pain; trunk muscle coactivation; onset detection; muscle activation; machine learning; neural networks; surface EMG; sEMG processing; force estimation; isometric contractions; surface EMG signal; co-contraction detection; muscular synergies; the time-frequency domain; wavelet transform; power spectral density; spectral estimation techniques; Welch method; Burg method; autoregressive model
• Accès en ligne: DOAB: download the publication; DOAB: description of the publication

Résumé

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.

Description matérielle

1 electronic resource (202 p.)

ISBN

books978-3-0365-7205-5
9783036572048
9783036572055

Accès

Open Access