Espinosa Loera, Ricardo Abel
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Espinosa Loera, Ricardo Abel
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Espinosa Loera, Ricardo Abel
Alternative Name
respinosa
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Scopus Author ID
57211517018

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Application of Convolutional Neural Networks for Fall Detection Using Multiple Cameras

2020 , Espinosa Loera, Ricardo Abel , Ponce, Hiram , Gutiérrez, Sebastián , Martinez-Villaseñor, Lourdes , Moya-Albor, Ernesto , Brieva, Jorge

Currently one of the most important research issue for artificial intelligence and computer vision tasks is the recognition of human falls. Due to the current exponential increase in the use of cameras is it common to use vision-based approach for fall detection and classification systems. On another hand deep learning algorithms have transformed the way that we see vision-based problems. The Convolutional Neural Network (CNN) as deep learning technique offers more reliable and robust solutions on detection and classification problems. Focusing only on a vision-based approach, for this work we used images from a new public multimodal data set for fall detection (UP-Fall Detection dataset) published by our research team. In this chapter we present fall detection system using a 2D CNN analyzing multiple camera information. This method analyzes images in fixed time window frames extracting features using an optical flow method that obtains information of relative motion between two consecutive images. For experimental results, we tested this approach in UP-Fall Detection dataset. Results showed that our proposed multi-vision-based approach detects human falls achieving 95.64% in accuracy with a simple CNN network architecture compared with other state-of-the-art methods.

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Click-event sound detection in automotive industry using machine/deep learning

2021 , Espinosa Loera, Ricardo Abel , Ponce, Hiram , Gutiérrez, Sebastián

In the automotive industry, despite the robotic systems on the production lines, factories continue employing workers in several custom tasks getting for semi-automatic assembly operations. Specifically, the assembly of electrical harnesses of engines comprises a set of connections between electrical components. Despite the task is easy to perform, employees tend not to notice that a few components are not being connected properly due to physical fatigue provoked by repetitive tasks. This yields a low quality of the assembly production line and possible hazards. In this work, we propose a sound detection system based on machine/deep learning (ML/DL) approaches to identify click sounds produced when electrical harnesses are connected. The purpose of this system is to count the number of connections properly made and to feedback to the employees. We collect and release a public dataset of 25,000 click sounds of 25 ms length at 22 kHz during three months of assembly operations in an automotive production line located in Mexico. Then, we design an ML/DL-based methodology for click sound detection of assembled harnesses under real conditions of a noisy environment (noise level ranging from −16.67 dB to −12.87 dB) including other machinery sounds. Our best ML/DL model (i.e., a combination between five acoustic features and an optimized convolutional neural network) is able to detect click sounds in a real assembly production line with an accuracy of 94.55±0.83 %. To the best of our knowledge, this is the first time a click sounds detection system in assembling electrical harnesses of engines for giving feedback to the workers is proposed and implemented in a real-world automotive production line. We consider this work valuable for the automotive industry on how to apply ML/DL approaches for improving the quality of semi-automatic assembly operations. © 2021 Elsevier B.V.

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A 3D orthogonal vision-based band-gap prediction using deep learning: A proof of concept

2022 , Espinosa Loera, Ricardo Abel , Ponce, Hiram , ORTIZ-MEDINA, JOSUE

In this work, a vision-based system for the electronic band-gap prediction of organic molecules is proposed using a multichannel 2D convolutional neural network (CNN) and a 3D CNN, applied to the recognition and classification of 2D projected images from 3D molecular structure models. The generated images are input into the CNN for an estimation of the energy gap, associated with the molecular structure. The public data set used in this research was the Organic Materials Database (OMDB-GAP1). A data transformation from the descriptive information contained in the data set to three 2D orthogonal images of molecules was done. The training set is composed of 30,000 images, whereas the testing set was composed of 7500 images, from 12,500 different molecules. The multichannel 2D CNN architecture was optimized via Bayesian optimization. Experimental results showed that the proposed CNN model obtained an acceptable mean absolute error of 0.6780 eV and root mean-squared error of 0.7673 eV, in contrast to two machine learning methods reported in the literature used for band-gap prediction based on conventional density function theory (DFT) methods. These results demonstrate the feasibility of CNN models to materials science routines using orthogonal images projections of molecules. © 2021 Elsevier B.V.

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