Ismael Gutierrez, Diego Naranjo, Isaac Tretta, Luis Valverde, Juan José Vargas, Gabriela Barrantes, Luis Quesada, Adrián Lara
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Recognizing daily-life activities using sensor-collected data in a kitchen
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
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