A global view on how local muscular fatigue affects human performance

• Autor(a) principal: Goethel, Marcio F.
• Data de Publicação: 2020
• Outros Autores: Goncalves, Mauro [UNESP], Brietzke, Cayque, Cardozo, Adalgiso C. [UNESP], Vilas-Boas, Joao P., Ervilha, Ulysses F.
• Tipo de documento: Artigo
• Idioma: eng
• Título da fonte: Repositório Institucional da UNESP
• Texto Completo: http://dx.doi.org/10.1073/pnas.2007579117; http://hdl.handle.net/11449/209447
• topic: artificial neural network; self-organizing feature maps; exercise; fatigue

There is a growing interest in scientific literature on identifying how and to what extent interventions applied to a specific body region influence the responses and functions of other seemingly unrelated body regions. To investigate such a construct, it is necessary to have a global multivariate model that considers the interaction among several variables that are involved in a specific task and how a local and acute impairment affects the behavior of the output of such a model. We developed an artificial neural network (ANN)-based multivariate model by using parameters of motor skills obtained from kinematic, postural control, joint torque, and proprioception variables to assess the local fatigue effects of the abductor hip muscles on the functional profile during a single-leg drop landing and a squatting task. Findings suggest that hip abductor muscles' local fatigue produces a significant effect on a general functional profile, built on different control systems. We propose that expanded and global approaches, such as the one used in this study, have great applicability and have the potential to serve as a tool that guaran-tees ecological validity of future investigations.