3D Convolutional Neural Network for Falling Detection using Only Depth Information

Sara Luengo Sánchez, Sergio de López Diz, David Fuentes-Jiménez, Cristina Losada-Gutiérrez, Marta Marrón-Romera, Ibrahim Sarker

Abstract

Nowadays, one of the major challenges global society is facing is population aging, which involves an increment of the medical expenses. Since falls are the major cause of injuries for elderly people, the need of a low-cost falling detector has increased rapidly over the years. In this context, we propose a fall-detection system based on 3D Convolutional Neural Networks (3D-CNN). Due to the fact that the system only uses depth information obtained by a RGB-D sensor placed in a overhead position to avoid occlusions, it results in a less invasive and intrusive fall-detection method for users than systems based on wearables. In addition, depth information preserves the privacy of people since they cannot be identified from this information. The 3D-CNN obtains spatial and temporal features from depth data, which allows to classify users’ actions and detect when a fall appears. Since there are no other available datasets for action recognition using only depth data from a top-view camera, the authors have recorded and labeled the GOTPD3, that has been made available to the scientific community. Thus, training and evaluation of the network has been carried out within the GOTPD3 dataset, and the achieved results validate the proposal.

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Paper Citation


in Harvard Style

Luengo Sánchez S., de López Diz S., Fuentes-Jiménez D., Losada-Gutiérrez C., Marrón-Romera M. and Sarker I. (2020). 3D Convolutional Neural Network for Falling Detection using Only Depth Information.In Proceedings of the 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 5: VISAPP, ISBN 978-989-758-402-2, pages 590-597. DOI: 10.5220/0009356205900597


in Bibtex Style

@conference{visapp20,
author={Sara Luengo Sánchez and Sergio de López Diz and David Fuentes-Jiménez and Cristina Losada-Gutiérrez and Marta Marrón-Romera and Ibrahim Sarker},
title={3D Convolutional Neural Network for Falling Detection using Only Depth Information},
booktitle={Proceedings of the 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 5: VISAPP,},
year={2020},
pages={590-597},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0009356205900597},
isbn={978-989-758-402-2},
}


in EndNote Style

TY - CONF

JO - Proceedings of the 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 5: VISAPP,
TI - 3D Convolutional Neural Network for Falling Detection using Only Depth Information
SN - 978-989-758-402-2
AU - Luengo Sánchez S.
AU - de López Diz S.
AU - Fuentes-Jiménez D.
AU - Losada-Gutiérrez C.
AU - Marrón-Romera M.
AU - Sarker I.
PY - 2020
SP - 590
EP - 597
DO - 10.5220/0009356205900597