Tecnologías vestibles para la seguridad en el trabajoun modelo basado en los equipos de protección individual inteligentes

  1. Sergio Márquez Sánchez
Supervised by:
  1. Juan Manuel Corchado Rodríguez Director
  2. Sara Rodríguez González Director

Defence university: Universidad de Salamanca

Year of defence: 2022

  1. Vicente J. Julián Inglada Chair
  2. Ana Belén Gil González Secretary
  3. Paulo Novais Committee member

Type: Thesis


The industrial sector is key to economic and social development; however, it is also known to entail certain risks for workers. To ensure their security, workers in the sector must be informed of and comply with industrial safety standards. Moreover, industries are required to transform their Personal Protective Equipment (PPE) and auxiliary systems by providing them with intelligence for timely risk identi cation, warnings, and decision-making. Arti cial Intelligence of Things, or AIoT for short, combines Arti cial Intelligence technologies with Internet of Things infrastructure to achieve more eficient Internet of Things (IoT) devices, creating great potential for industrial use. In addition, platforms that integrate Edge Computing technology have the ability to preprocess data and lter those that need to be sent from the IoT layer to the Cloud. This not only solves problems related to data privacy and security but also reduces Cloud service costs. Thus, in this thesis, the joint use of AIoT and wearable technology is proposed through a set of key techniques, devices and a platform for worker monitoring and risk prevention in an industrial setting. The proposed solution has a holistic approach aimed at creating a much more favorable environment for the personal protection of workers. Prior to developing the proposed platform, an analysis has been carried out of the state-of-the-art solutions and equipment found in the literature, speci cally, of the solutions that could be integrated in a system for the optimized prevention of risks and detection of health conditions. The developed platform has a modular design, combining the use of wearable technology, IoT electronic devices, Arti cial Intelligence and Edge Computing. The results obtained in this thesis evidence that the proposed platform's capabilities surpass other solutions developed to date; the platform has the ability to receive data, emit alarms, measure body parameters, recognize human activity, detect pollutants and anomalous situations, creating safer working conditions.