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

Abstract

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