Smart Helmet 5.0 for Industrial Internet of Things Using Artificial Intelligence

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.

A Model for Working Environment Monitoring in Smart Manufacturing

“…At the perception layer (I) WPAN, Wi-Fi, and Ethernet (wired) sensor nodes are deployed; the transport layer (II) contains microcontroller boards and sensor nodes with the ability to convert data transfer protocol from the wireless protocol from the perception layer, to IoT protocols applicable for the core of the system, in this case the MQTT protocol. The middleware layer (III) contains components such as Mosquitto MQTT broker (6), which handles the messaging bus, DB read/write to data store module 7, and an analytics module (8); the application layer contains a user application API (9) and alarm module (10).…”

BeSafe B2.0 Smart Multisensory Platform for Safety in Workplaces

“…However, for the Falls class, the simulation had to be performed by different workers on different surfaces, as can be seen, and it was the class with the smallest number of data. Now that we know the input for our model we proceed to train the LSTM, of which we have the following characteristics: we have 200 time steps, with a training set dimension ∈ R 3 (12,670,200,3), it is a sequential model, where we have 128 units for the LSTM, our learning rate 0.5, a hidden layer with activation function relu and of course output layer with so f tmax it is already a multiclass classification problem. We train it with 100 epochs where, as shown in Figure 14, you can see the history of the loss in the training and testing set.…”

“…The developed deeptech product is an AIoT (Artificial Intelligent IoT) platform that combines Artificial Intelligence and Internet of Things strategies with real-time data acquisition to generate valuable knowledge. In the first version of BeSafe [ 3 ], a platform based on rules through decision trees had been presented. However, no relationships displaying linear behavior had been found; for this reason, this research focuses on the analysis of the datasets acquired by the sensors in the bracelet, implementing human activity recognition combined with other unsupervised learning algorithms to detect anomalies.…”

“…Among the accidents with sick leave, 3542 were serious incidents, and 489 were fatal accidents [4]. Depending on the work environment, there are greater or lesser risks which must be carefully analyzed [3,5,6]. Some professions involve the performance of high-risk tasks, where there is a possibility of lethal accidents occurring.…”

Applications of Smart Helmet in Applied Sciences: A Systematic Review

“…A total of 22 academic papers that deal with the applications of smart helmets for workers were published during the past 11 years [8,9,[90][91][92][93][94][95][96][97][98][99][100][101][102][103][104][105][106][107][108][109] and are summarized in Table 3. The application fields include mining, construction, petrochemistry, disaster prevention, and medical treatment.…”