ABSTRACT
Federal regulations require employees to protect themselves from electrical hazards when working at substations. Such protections, commonly called personal protective equipment (PPE), vary with the hazard types and nature of exposure or delivery. Over the past decades, personal injuries and fatalities from electrical hazards have remained relatively common despite regular risk assessments and controls. One reason for this is that adequate PPE is not appropriately used. Easy-to-deploy strategies to detect proper use of PPE for electrical hazards are not available. Here, an intelligent detection model is developed to check whether PPE is appropriately worn or not;warning alarms would be triggered when the usage does not follow safety regulations. Arc-flash analysis is employed to determine a reasonable and safe PPE guideline. Eight types of PPE are considered, which cover the major PPE categories utilized in practice, including medical masks recommended for the Covid-19 pandemic. The model's framework utilizes a few-shot based graph neural network (GNN) technique to detect PPE. In contrast to prior data-driven models, only 50 images were collected for each PPE type, a relatively small number compared with state-of-the-art research. The proposed model was trained with diversified samples within multiple environments, resulting in a robust, efficient, intelligent detection model with probability of similarity in the range of 79%- 100%. To tackle the existing issues of computer-vision based PPE detection models, some technical suggestions on preserving personal privacy and PPE labels are provided. IEEE
ABSTRACT
Background: The Internet of Medical Things (IoMT) is now being connected to medical equipment to make patients more comfortable, offer better and more affordable health care options, and make it easier for people to get good care in the comfort of their own homes. Objective(s): The primary purpose of this study is to highlight the architecture and use of IoMT (Internet of Medical Things) technology in the healthcare system. Method(s): Several sources were used to acquire the material, including review articles published in various journals that had keywords such as, Internet of Medical Things, Wireless Fidelity, Remote Healthcare Monitoring (RHM), Point-of-care testing (POCT), and Sensors. Result(s): IoMT has succeeded in lowering both the cost of digital healthcare systems and the amount of energy they use. Sensors are used to measure a wide range of things, from physiological to emotional responses. They can be used to predict illness before it happens. Conclusion(s): The term "Internet of Medical Things" refers to the broad adoption of healthcare solutions that may be provided in the home. Making such systems intelligent and efficient for timely prediction of important illnesses has the potential to save millions of lives while decreasing the burden on conventional healthcare institutions, such as hospitals. patients and physicians may now access real-time data due to advancements in IoM. Copyright © 2022 Wal et al.
ABSTRACT
Since 2020, the coronavirus disease (COVID-19) pandemic has had a substantial impact on all community sectors worldwide, particularly the health care sector. Healthcare workers (HCWs) are at risk of COVID-19 infection due to occupational exposure to infectious patients, visitors, and staff. Contact tracing of close physical interaction is an essential control measure, especially in hospitals, to prevent onward transmission during an outbreak event. In this article, we propose an IoT-based contact tracing system for subject identification, interaction tracking and data transmission in hospital wards. The system, based on Bluetooth Low Energy (BLE) devices, tracks the duration of interactions between different HCWs, and the time each HCW spends within the patient rooms using additional information from proximity sensors in the hallway or on the door frame of the patient room. The collected data are transferred via Long Range (LoRa) wireless technology and further analyzed to inform infection prevention activities. The suggested system’s performance is evaluated in a COVID-19 patient ward with both standard and negative pressure isolation rooms, and the current system’s capabilities and future research prospects are briefly discussed. IEEE
ABSTRACT
Visually impaired people require support with regular tasks including navigating, detecting obstacles, and maintaining safety, especially in both indoor and outdoor environments. As a result of the advancement of assistive technology, their lives have become substantially more convenient. Here, cutting-edge assistive devices and technologies for the visually impaired are reviewed, along with a chronology of their evolution. These methodologies are classified according to their intended applications. The taxonomy is combined with a description of the tests and experiments that can be used to examine the characteristics and assessments of assistive technology. In addition, the algorithms used in assistive devices are examined. This paper looks at solar industry innovations and promotes using renewable energy sources to create assistive devices, as well as, addresses the sudden advent of COVID-19 and the shift in the development of assistive devices. This review can serve as a stepping stone for further research on the topic. Copyright © 2022 The Author(s)
ABSTRACT
The last two years were strongly shaped by the COVID-19 pandemic and the social distancing countermeasures. The worldwide research changed as well, focusing on the problems created or exacerbated by the novel coronavirus. The Pervasive Electromagnetics Lab of the Tor Vergata University of Rome with a great engagement of several medical engineering students focused on applying sensor-oriented RFID to improve personal safety. In particular, the sensorization of the filtering facepiece respirators (FFRs) was one of the COVID-inspired research topics. FFRs integrating RFID-based sensors were designed and tested. In this contribution, the most significant results achieved are summarized regarding humidity-sensing and cough-monitoring FFRs.