Translation of surface electromyography to clinical and motor rehabilitation applications: The need for new clinical figures

Abstract: Advanced sensors/electrodes and signal processing techniques provide powerful tools to analyze surface electromyographic signals (sEMG) and their features, to decompose sEMG into the constituent motor unit action potential trains, and to identify synergies, neural muscle drive, and EEG–sEMG coherence. However, despite thousands of articles, dozens of textbooks, tutorials, consensus papers, and European and International efforts, the translation of this knowledge into clinical activities and assessment procedures has been very slow, likely because of lack of clinical studies and competent operators in the field. Understanding and using sEMG-based hardware and software tools requires a level of knowledge of signal processing and interpretation concepts that is multidisciplinary and is not provided by most academic curricula in physiotherapy, movement sciences, neurophysiology, rehabilitation, sport, and occupational medicine. The chasm existing between the available knowledge and its clinical applications in this field is discussed as well as the need for new clinical figures. The need for updating the training of physiotherapists, neurophysiology technicians, and clinical technologists is discussed as well as the required competences of trainers and trainees. Indications and examples are suggested and provide a basis for addressing the problem. Two teaching examples are provided in the Supplementary Material.

Angaben zum Objekt

• Standort: Deutsche Nationalbibliothek Frankfurt am Main
• Umfang: Online-Ressource
• Sprache: Englisch

Verweise und Beziehungen

• Erschienen in: Translation of surface electromyography to clinical and motor rehabilitation applications: The need for new clinical figures ; volume:14 ; number:1 ; year:2023 ; extent:16 ; Translational Neuroscience ; 14, Heft 1 (2023) (gesamt 16)

Beteiligte, Orts- und Zeitangaben

• Urheber: Merletti, Roberto ; Temporiti, Federico ; Gatti, Roberto ; Gupta, Sanjeev ; Sandrini, Giorgio ; Serrao, Mariano

Weitere Informationen

• DOI: 10.1515/tnsci-2022-0279
• URN: urn:nbn:de:101:1-2023031513070326022758
• Rechteinformation: Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
• Letzte Aktualisierung: 14.08.2025, 10:53 MESZ