Recognizing daily-life activities using sensor-collected data in a kitchen

Descripción:

This paper focuses on the recognition and classification of Activities of Daily Living (ADLs) that are carried out in a kitchen. To do this, a Recurrent Neural Network architecture of the Long-Short Term Memory (LSTM) type is implemented as a classifier. The ARAS dataset is used for training and evaluation. A classifier is obtained with an average value in the F1 metric of 95.33% for the chosen data set.

Tipo de publicación: Conference Paper

Publicado en: 2021 IEEE V Jornadas Costarricenses de Investigación en Computación e Informática (JoCICI)

Autores
  • Ismael Gutierrez
  • Diego Naranjo
  • Isaac Tretta
  • Luis Valverde
  • Juan Jose Vargas
  • Gabriela Barrantes
  • Luis Quesada
  • Adrian Lara

Investigadores del CITIC asociados a la publicación
Juan José Vargas Morales
E. Gabriela Barrantes Sliesarieva
Luis Quesada Quirós
Adrian Lara Petitdemange

Proyecto asociado a la publicación

DOI BIBTEXT

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Cita bibliográfica
Recognizing daily-life activities using sensor-collected data in a kitchen