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Feature selection by genetic algorithms for classification of running patterns in high-performance sports

Grant number: 20/01022-1
Support Opportunities:Scholarships in Brazil - Scientific Initiation
Effective date (Start): May 01, 2020
Effective date (End): December 31, 2020
Field of knowledge:Physical Sciences and Mathematics - Computer Science - Computing Methodologies and Techniques
Principal Investigator:Renato Tinós
Grantee:Sergio Baldo Junior
Host Institution: Faculdade de Filosofia, Ciências e Letras de Ribeirão Preto (FFCLRP). Universidade de São Paulo (USP). Ribeirão Preto , SP, Brazil


The identification of running patterns, especially those associated with fatigue, can assist in designing more efficient workouts and preventing injuries in high-performance sports. However, this task is not trivial due to the complexity of the patterns. An interesting alternative is to use Machine Learning methods, such as Artificial Neural Networks (ANNs), to classify running patterns. In this work, force signals emitted by load cells coupled at the base of a treadmill will be classified by an ANN. It is possible to extract many features (attributes) from these signals, and it is important to identify which ones are most relevant to the classification performed by the ANN. Therefore, this project aims to use Genetic Algorithms to find the best subsets of features extracted from force signals to be used as RNA inputs. (AU)

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