Using Deep Neural Networks for Human Fall Detection Based on Pose Estimation

• Tipo: Artigo em Revista Científica Internacional
• Data: 2022-06

Autores

Mohammadamin Salimi (Autor) Outra; José J.M. Machado (Autor) FEUP; João Manuel R. S. Tavares (Autor) FEUP

Revista

Título: Sensors

Vol. 22 Nº 4

Páginas: 4544-4544

ISSN: 1424-3210

Editora: MDPI

Indexação

ISI Web of Knowledge - 0 Citações

ISI Web of Science

Scopus

Classificação Científica

CORDIS: Ciências Tecnológicas

FOS: Ciências da engenharia e tecnologias

Projetos Associados

Safe Cities - Safe Cities - Inovação para Construir Cidades Seguras

Outras Informações

ID Authenticus: P-00W-THA

DOI: 10.3390/s22124544

Abstract (EN): Requests for caring for and monitoring the health and safety of older adults are increasing nowadays and form a topic of great social interest. One of the issues that lead to serious concerns is human falls, especially among aged people. Computer vision techniques can be used to identify fall events, and Deep Learning methods can detect them with optimum accuracy. Such imaging-based solutions are a good alternative to body-worn solutions. This article proposes a novel human fall detection solution based on the Fast Pose Estimation method. The solution uses Time-Distributed Convolutional Long Short-Term Memory (TD-CNN-LSTM) and 1Dimentional Convolutional Neural Network (1D-CNN) models, to classify the data extracted from image frames, and achieved high accuracies: 98 and 97% for the 1D-CNN and TD-CNN-LSTM models, respectively. Therefore, by applying the Fast Pose Estimation method, which has not been used before for this purpose, the proposed solution is an effective contribution to accurate human fall detection, which can be deployed in edge devices due to its low computational and memory demands.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 15