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Article

Smart Helmet 5.0 for Industrial Internet of Things Using Artificial Intelligence

1
Laboratoire de l’Informatique du Parallélisme 46 allée d’Italie, 69007 Lyon, France
2
BISITE Research Group, University of Salamanca, Calle Espejo s/n. Edificio Multiusos I+D+i, 37007 Salamanca, Spain
3
Graduate School in Information Technology and Communications Research Department, Universidad Politécnica de Pachuca, Zempoala Hidalgo 43830, Mexico
4
Air Institute, IoT Digital Innovation Hub (Spain), 37188 Salamanca, Spain
5
Department of Electronics, Information and Communication, Faculty of Engineering, Osaka Institute of Technology, Osaka 535-8585, Japan
6
Faculty of Creative Technology & Heritage, Universiti Malaysia Kelantan, Locked Bag 01, 16300 Bachok, Malaysia
*
Author to whom correspondence should be addressed.
These authors contributed equally to this work.
Sensors 2020, 20(21), 6241; https://doi.org/10.3390/s20216241
Received: 10 September 2020 / Revised: 26 October 2020 / Accepted: 27 October 2020 / Published: 1 November 2020
Information and communication technologies (ICTs) have contributed to advances in Occupational Health and Safety, improving the security of workers. The use of Personal Protective Equipment (PPE) based on ICTs reduces the risk of accidents in the workplace, thanks to the capacity of the equipment to make decisions on the basis of environmental factors. Paradigms such as the Industrial Internet of Things (IIoT) and Artificial Intelligence (AI) make it possible to generate PPE models feasibly and create devices with more advanced characteristics such as monitoring, sensing the environment and risk detection between others. The working environment is monitored continuously by these models and they notify the employees and their supervisors of any anomalies and threats. This paper presents a smart helmet prototype that monitors the conditions in the workers’ environment and performs a near real-time evaluation of risks. The data collected by sensors is sent to an AI-driven platform for analysis. The training dataset consisted of 11,755 samples and 12 different scenarios. As part of this research, a comparative study of the state-of-the-art models of supervised learning is carried out. Moreover, the use of a Deep Convolutional Neural Network (ConvNet/CNN) is proposed for the detection of possible occupational risks. The data are processed to make them suitable for the CNN and the results are compared against a Static Neural Network (NN), Naive Bayes Classifier (NB) and Support Vector Machine (SVM), where the CNN had an accuracy of 92.05% in cross-validation. View Full-Text
Keywords: PPE; OHS; risk detection; naive Bayes; support vector machine; convolutional neural network; deep learning; microcontroller PPE; OHS; risk detection; naive Bayes; support vector machine; convolutional neural network; deep learning; microcontroller
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MDPI and ACS Style

Campero-Jurado, I.; Márquez-Sánchez, S.; Quintanar-Gómez, J.; Rodríguez, S.; Corchado, J.M. Smart Helmet 5.0 for Industrial Internet of Things Using Artificial Intelligence. Sensors 2020, 20, 6241. https://doi.org/10.3390/s20216241

AMA Style

Campero-Jurado I, Márquez-Sánchez S, Quintanar-Gómez J, Rodríguez S, Corchado JM. Smart Helmet 5.0 for Industrial Internet of Things Using Artificial Intelligence. Sensors. 2020; 20(21):6241. https://doi.org/10.3390/s20216241

Chicago/Turabian Style

Campero-Jurado, Israel, Sergio Márquez-Sánchez, Juan Quintanar-Gómez, Sara Rodríguez, and Juan M. Corchado. 2020. "Smart Helmet 5.0 for Industrial Internet of Things Using Artificial Intelligence" Sensors 20, no. 21: 6241. https://doi.org/10.3390/s20216241

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