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Machine learning algorithms for the evaluation of the effect of inspiratory muscle training on the aerobic response in recreational cyclists

Grant number: 19/10749-5
Support type:Scholarships in Brazil - Post-Doctorate
Effective date (Start): September 01, 2019
Effective date (End): August 31, 2021
Field of knowledge:Health Sciences - Physiotherapy and Occupational Therapy
Principal Investigator:Aparecida Maria Catai
Grantee:Patrícia Rehder dos Santos
Home Institution: Centro de Ciências Biológicas e da Saúde (CCBS). Universidade Federal de São Carlos (UFSCAR). São Carlos , SP, Brazil
Associated research grant:16/22215-7 - Impact of inspiratory muscle training and aging on metabolic maping and autonomic modulation at rest and on cardiovascular, respiratory and metabolic responses to exercise in healthy men, AP.TEM

Abstract

Low levels of aerobic fitness may be associated with limitations in the supply of oxygen to the active muscles. With the advancement of signal processing techniques, such as machine learning, the dynamic response of the aerobic system can be evaluated systematically, before and after therapeutic interventions. New analysis tools can be used to detect changes in general physical health with direct applicability in physical intervention programs, such as the one proposed here. It is expected that new machine learning algorithms will be able to better understand effects of inspiratory muscle training (IMT) on the aerobic response. By combining variables related to peripheral and central responses, in combination with pulmonary oxygen consumption (p.VO2pico), it will be possible to investigate the exact influence of the training proposed on the aerobic response.