A Profa. Dra. Patricia Takako Endo teve o artigo “Accelerometer-Based Human Fall Detection Using Convolutional Neural Networks” aceito e publicado no periódico Sensors, ISSN 1424-8220 (Qualis A1).
O trabalho é resultado de uma colaboração de pesquisa entre a Universidade de Pernambuco, Universidade Federal de Pernambuco, Universidade Federal do Rio Grande do Norte e Dublin City University.
O primeiro autor, Guto Leoni, bem com os autores Kayo Monteiro e Élisson Silva, foram alunos de graduação da UPE Caruaru, do bacharelado em Sistemas de Informação.
Título: Accelerometer-Based Human Fall Detection Using Convolutional Neural Networks
Resumo: Human falls are a global public health issue resulting in over 37.3 million severe injuries and 646,000 deaths yearly. Falls result in direct financial cost to health systems and indirectly to society productivity. Unsurprisingly, human fall detection and prevention are a major focus of health research. In this article, we consider deep learning for fall detection in an IoT and fog computing environment. We propose a Convolutional Neural Network composed of three convolutional layers, two maxpool, and three fully-connected layers as our deep learning model. We evaluate its performance using three open data sets and against extant research. Our approach for resolving dimensionality and modelling simplicity issues is outlined. Accuracy, precision, sensitivity, specificity, and the Matthews Correlation Coefficient are used to evaluate performance. The best results are achieved when using data augmentation during the training process. The paper concludes with a discussion of challenges and future directions for research in this domain.
Autores: Guto Leoni Santos,Patricia Takako Endo, Kayo Henrique de Carvalho Monteiro, Elisson da Silva Rocha, Ivanovitch Silva e Theo Lynn
Link para o artigo: https://www.mdpi.com/