RT Journal Article
T1 BERT for Activity Recognition Using Sequences of Skeleton Features and Data Augmentation with GAN
A1 Ramirez, Heilym
A1 Velastin Carroza, Sergio Alejandro
A1 Cuéllar, Sara
A1 Fabregas, Ernesto
A1 Farias, Gonzalo
AB Recently, the scientific community has placed great emphasis on the recognition of human activity, especially in the area of health and care for the elderly. There are already practical applications of activity recognition and unusual conditions that use body sensors such as wrist-worn devices or neck pendants. These relatively simple devices may be prone to errors, might be uncomfortable to wear, might be forgotten or not worn, and are unable to detect more subtle conditions such as incorrect postures. Therefore, other proposed methods are based on the use of images and videos to carry out human activity recognition, even in open spaces and with multiple people. However, the resulting increase in the size and complexity involved when using image data requires the use of the most recent advanced machine learning and deep learning techniques. This paper presents an approach based on deep learning with attention to the recognition of activities from multiple frames. Feature extraction is performed by estimating the pose of the human skeleton, and classification is performed using a neural network based on Bidirectional Encoder Representation of Transformers (BERT). This algorithm was trained with the UP-Fall public dataset, generating more balanced artificial data with a Generative Adversarial Neural network (GAN), and evaluated with real data, outperforming the results of other activity recognition methods using the same dataset.
PB MDPI
SN 1424-3210
YR 2023
FD 2023-02-01
LK https://hdl.handle.net/10016/37440
UL https://hdl.handle.net/10016/37440
LA eng
NO This research was supported in part by the Chilean Research and Development Agency (ANID) under Project FONDECYT 1191188, The National University of Distance Education under Projects 2021V/-TAJOV/00 and OPTIVAC 096-034091 2021V/PUNED/008, and the Ministry of Science and Innovation of Spain under Project PID2019-108377RB-C32.
DS e-Archivo
RD 30 jun. 2024