Vision-based fall detection systems have experienced fast development over the last years. To determine the course of its evolution and help new researchers, the main audience of this paper, a comprehensive revision of all published articles in the main scientific databases regarding this area during the last five years has been made. After a selection process, detailed in the Materials and Methods Section, eighty-one systems were thoroughly reviewed. Their characterization and classification techniques were analyzed and categorized. Their performance data were also studied, and comparisons were made to determine which classifying methods best work in this field. The evolution of artificial vision technology, very positively influenced by the incorporation of artificial neural networks, has allowed fall characterization to become more resistant to noise resultant from illumination phenomena or occlusion. The classification has also taken advantage of these networks, and the field starts using robots to make these systems mobile. However, datasets used to train them lack real-world data, raising doubts about their performances facing real elderly falls. In addition, there is no evidence of strong connections between the elderly and the communities of researchers.

This paper reports on a state-of-the-art remote laboratory project called Virtual Instrument Systems in Reality (VISIR). VISIR allows wiring and measuring of electronic circuits remotely on a virtual workbench that replicates physical circuit breadboards. The wiring mechanism is developed by means of a relay switching matrix connected to a PCI eXtensions for Instrumentation (PXI) instrumentation platform. The entire equipment is controlled by LabVIEW server software, in addition to a measurement server software that protects the equipment from hazard connections by verifying input circuit designs, sent by students, before being executed. This paper addresses other approaches such as remote labs based on Data Acquisition Cards (DAQs), NetLab, and RemotElectLab, comparing them with VISIR in order to emphasize its singularity. Topics discussed are as follows: the technical description, software, operation cycle, features, and provided services. In addition, the feedback received by students at several universities and the encountered drawbacks along with the proposed solutions are highlighted. The paper finally addresses the ongoing and future challenges within the VISIR community including its integration with Learning Management Systems (LMSs) and iLab Shared Architecture (ISA), its new hardware version release that is based on LAN eXtensions for Instrumentation (LXI), and its new open platform version that supports federated access.

This research introduces a conceptual framework designed to enhance worker safety and well-being in industrial environments, such as oil and gas construction plants, by leveraging Human Digital Twin (HDT) cutting-edge technologies and advanced artificial intelligence (AI) techniques. At its core, this study is in the developmental phase, aiming to create an integrated system that could enable real-time monitoring and analysis of the physical, mental, and emotional states of workers. It provides valuable insights into the impact of Digital Twins (DT) technology and its role in Industry 5.0. With the development of a chatbot trained as an empathic evaluator that analyses emotions expressed in written conversations using natural language processing (NLP); video logs capable of extracting emotions through facial expressions and speech analysis; and personality tests, this research intends to obtain a deeper understanding of workers’ psychological characteristics and stress levels. This innovative approach might enable the identification of stress, anxiety, or other emotional factors that may affect worker safety. Whilst this study does not encompass a case study or an application in a real-world setting, it lays the groundwork for the future implementation of these technologies. The insights derived from this research are intended to inform the development of practical applications aimed at creating safer work environments.

Small and medium sized enterprises (SMEs) assure economic growth in Europe. Generally, many SMEs are struggling to survive in an ongoing global recession and are often reluctant to release or pay for staff training. Cloud Computing offers many opportunities and could help companies to improve their business and use technology more efficiently In this chapter, learning methods particularly E-Learning in European SMEs is described in part 1. In this paper a short presentation of Cloud Computing and advantages for SMEs (part 1), the objectives of the European project IN-CLOUD (part 2) and the work in progress within this project are presented