Real-time Gait Pattern Classification Using Artificial Neural Networks

Diego Robles, Mouna Benchekroun, Andrea Lira, Carla Taramasco, Vincent Zalc, Igor Irazzoky, Dan Istrate

Resultado de la investigación: Contribución a los tipos de informe/libroContribución a la conferenciarevisión exhaustiva

Resumen

The expression of human gait associated with neurological disorders is difficult to describe and is characterized by fluctuating predominance in the presence of complex movement patterns. The analysis of human gait patterns can provide significant information related to the physical and neurological functions of individuals, and may contribute to the diagnosis of human motor disorders in pathological conditions.The present study seeks to determine the classification capacity of different types of simulated abnormal gait patterns by recording the accelerations of the center of mass, the extraction of characteristics in the time and frequency domain and the classification based on the use of artificial neural networks in real time.

Idioma originalInglés
Título de la publicación alojada2022 IEEE International Workshop on Metrology for Living Environment, MetroLivEn 2022 - Proceedings
EditorialInstitute of Electrical and Electronics Engineers Inc.
Páginas76-80
Número de páginas5
ISBN (versión digital)9781665408936
DOI
EstadoPublicada - 2022
Publicado de forma externa
Evento2022 IEEE International Workshop on Metrology for Living Environment, MetroLivEn 2022 - Cosenza, Italia
Duración: 25 may. 202227 may. 2022

Serie de la publicación

Nombre2022 IEEE International Workshop on Metrology for Living Environment, MetroLivEn 2022 - Proceedings

Conferencia

Conferencia2022 IEEE International Workshop on Metrology for Living Environment, MetroLivEn 2022
País/TerritorioItalia
CiudadCosenza
Período25/05/2227/05/22

Áreas temáticas de ASJC Scopus

  • Edificación y construcción
  • Instrumental
  • Factores humanos y ergonomía

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