Smart Helmet 5.0 for Industrial Internet of Things Using Artificial Intelligence

TLDR

ICTs, IIoT, and AI have enabled advanced PPE that continuously monitors work environments, detects risks, and alerts workers and supervisors to anomalies. The study introduces a smart helmet prototype that uses AI to monitor environmental conditions and evaluate occupational risks in near real‑time, employing a CNN for risk detection. Sensors collect data that are transmitted to an AI platform trained on 11,755 samples across 12 scenarios, where supervised models—including a CNN, static NN, Naïve Bayes, and SVM—are compared. The CNN achieved 92.05 % accuracy in cross‑validation, outperforming the other models.

Abstract

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.