Your browser doesn't support javascript.
loading
Optimal Identification of Muscle Synergies From Typical Sit-to-Stand Clinical Tests.
Ranaldi, Simone; Gizzi, Leonardo; Severini, Giacomo; De Marchis, Cristiano.
Afiliação
  • Ranaldi S; Deparment of Industrial, Electronics and Mechanical EngineeringRoma Tre University 00154 Rome Italy.
  • Gizzi L; Institute for Modelling and Simulation of Biomechanical SystemsUniversity of Stuttgart 70174 Stuttgart Germany.
  • Severini G; School of Electrical and Electronic EngineeringUniversity College Dublin 4 Dublin Ireland.
  • De Marchis C; Department of EngineeringUniversity of Messina 98122 Messina Italy.
IEEE Open J Eng Med Biol ; 4: 31-37, 2023.
Article em En | MEDLINE | ID: mdl-37063235
Goal: The goal of this manuscript is to investigate the optimal methods for extracting muscle synergies from a sit-to-stand test; in particular, the performance in identifying the modular structures from signals of different length is characterized. Methods: Surface electromyography signals have been recorded from instrumented sit-to-stand trials. Muscle synergies have then been extracted from signals of different duration (i.e. 5 times sit to stand and 30 seconds sit to stand) from different portions of a complete sit-to-stand-to-sit cycle. Performance have then been characterized using cross-validation procedures. Moreover, an optimal method based on a modified Akaike Information Criterion measure is applied on the signal for selecting the correct number of synergies from each trial. Results: Results show that it is possible to identify correctly muscle synergies from relatively short signals in a sit-to-stand experiment. Moreover, the information about motor control structures is identified with a higher consistency when only the sit-to-stand phase of the complete cycle is considered. Conclusions: Defining a set of optimal methods for the extraction of muscle synergies from a clnical test such as the sit-to-stand is of key relevance to ensure the applicability of any synergy-related analysis in the clinical practice, without requiring knowledge of the technical signal processing methods and the underlying features of the signal.
Palavras-chave

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Diagnostic_studies / Prognostic_studies Idioma: En Ano de publicação: 2023 Tipo de documento: Article

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Diagnostic_studies / Prognostic_studies Idioma: En Ano de publicação: 2023 Tipo de documento: Article