Open AccessDeep Learning and Uniform LBP Histograms for Position Recognition of Elderly People with Privacy Preservation
Author(s) -
Monia Hamdi,
Heni Bouhamed,
Abeer D. Algarni,
Hela Elmannai,
Souham Meshoul
Publication year - 2021
Publication title -
international journal of computers, communications and control
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.422
H-Index - 33
eISSN - 1841-9844
pISSN - 1841-9836
DOI - 10.15837/ijccc.2021.5.4256
Subject(s) - computer science , artificial intelligence , local binary patterns , histogram , convolutional neural network , context (archaeology) , machine learning , deep learning , novelty , feature extraction , population , pattern recognition (psychology) , image (mathematics) , medicine , paleontology , philosophy , theology , environmental health , biology
For the elderly population, falls are a vital health problem especially in the current context of home care for COVID-19 patients. Given the saturation of health structures, patients are quarantined, in order to prevent the spread of the disease. Therefore, it is highly desirable to have a dedicated monitoring system to adequately improve their independent living and significantly reduce assistance costs. A fall event is considered as a specific and brutal change of pose. Thus, human poses should be first identified in order to detect abnormal events. Prompted by the great results achieved by the deep neural networks, we proposed a new architecture for image classification based on local binary pattern (LBP) histograms for feature extraction. These features were then saved, instead of saving the whole image in the series of identified poses. We aimed to preserve privacy, which is highly recommended in health informatics. The novelty of this study lies in the recognition of individuals’ positions in video images avoiding the convolution neural networks (CNNs) exorbitant computational cost and Minimizing the number of necessary inputs when learning a recognition model. The obtained numerical results of our approach application are very promising compared to the results of using other complex architectures like the deep CNNs.