Physical Activity Classification Using an Artificial Neural Networks Based on the Analysis of Anthropometric Measurements

Antonio J. Alvarez, Erika Severeyn, Sara Wong, Héctor Herrera, Jesús Velásquez, Alexandra La Cruz

Resultado de la investigación: Capítulo del libro/informe/acta de congresoContribución a la conferenciarevisión exhaustiva

Resumen

Physical activity is one of the most important factors in leading a healthy life, which has increased the interest in the scientific community to evaluate methods and tools that can help people maintain an exercise routine, such as portable devices that can track the movements of the user and provide an appropriate feedback. Interest has also emerged in assessing the discrimination between physically active and inactive persons through the use of readily available data, which is the aim of this work. In this case, we used an auto-encoder to find the most outstanding characteristics of an anthropometric data set, in order to get the most representative attributes. Then use them to train an Artificial Neural Network (ANN), so that it could learn to identify between a physically active and a sedentary person. The ANN obtained 81% accuracy, 82% precision, 88% recall, 83% F1 score and 0.89 AUC. These results position the ANN as a viable model that could be used as a tool in scenarios such as customer profiling for different interested companies.

Idioma originalInglés
Título de la publicación alojadaSystems and Information Sciences - Proceedings of ICCIS 2020
EditoresMiguel Botto-Tobar, Willian Zamora, Johnny Larrea Plúa, José Bazurto Roldan, Alex Santamaría Philco
EditorialSpringer Science and Business Media Deutschland GmbH
Páginas60-70
Número de páginas11
ISBN (versión impresa)9783030591939
DOI
EstadoPublicada - 2021
Evento1st International Conference on Systems and Information Sciences, ICCIS 2020 - Manta, Ecuador
Duración: 27 jul 202029 jul 2020

Serie de la publicación

NombreAdvances in Intelligent Systems and Computing
Volumen1273 AISC
ISSN (versión impresa)2194-5357
ISSN (versión digital)2194-5365

Conferencia

Conferencia1st International Conference on Systems and Information Sciences, ICCIS 2020
País/TerritorioEcuador
CiudadManta
Período27/07/2029/07/20

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