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100   $a20230623d2023      uy             0
101 0 $aeng
135   $aur|||||||||||
181   $ctxt$2rdacontent
182   $cc$2rdamedia
183   $acr$2rdacarrier
200 00$aElectromyography signal acquisition and processing for movement analysis /$fedited by Francesco Di Nardo, Valentina Agostini, Silvia Conforto
210  1$aBasel, Switzerland :$cMDPI - Multidisciplinary Digital Publishing Institute,$d2023.
215   $a1 online resource (202 pages)
311   $a3-0365-7204-X 
330   $aThis 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.
606   $aElectromyography
615  0$aElectromyography.
676   $a616.7407547
702   $aDi Nardo$b Francesco
702   $aAgostini$b Valentina
702   $aConforto$b Silvia
801  0$bNjHacI
801  1$bNjHacl
906   $aBOOK
912   $a9910719772603321
996   $aElectromyography Signal Acquisition and Processing for Movement Analysis$93360305
997   $aUNINA