Advancing Medical Education through the cINnAMON Web Application

The cINnAMON EUREKA Traditional project endeavours to revolutionize indoor lighting positioning and monitoring through the integration of intelligent devices and advanced sensor technologies. This article presents the prototypes developed for various project components and explores their potential application in medical education, particularly for aspiring healthcare professionals. The current variant of the intelligent bulb prototype offers a comparative analysis of the project's bulb against commercially available smart bulbs, shedding light on its superior efficiency and capabilities. Furthermore, the initial smart bracelet prototype showcases its ability to collect and analyse data from an array of built-in sensors, empowering medical students to evaluate fragility levels based on accelerometer, gyroscope, orientation, and heart rate data. Leveraging trilateration and optimization algorithms, the intelligent location module enables precise monitoring of individuals' positions within a building, enhancing medical students' understanding of patient localization in healthcare settings. In addition, the recognition of human activity module harnesses data from the bracelet's sensors to classify different activities, providing medical students with invaluable insights into patients' daily routines and mobility patterns. The user's personal profile module facilitates seamless user registration and access to the comprehensive services offered by the cINnAMON system, empowering medical students to collect patient data for analysis and aiding doctors in making informed healthcare decisions. With the telemonitoring system, medical students can remotely monitor patients by configuring sensors in their homes, thus enabling a deeper understanding of remote patient management.Comment: 10 pages, 7 figure

Intelligent Luminaire based Real-time Indoor Positioning for Assisted Living

This paper presents an experimental evaluation on the accuracy of indoor localisation. The research was carried out as part of a European Union project targeting the creation of ICT solutions for older adult care. Current expectation is that advances in technology will supplement the human workforce required for older adult care, improve their quality of life and decrease healthcare expenditure. The proposed approach is implemented in the form of a configurable cyber-physical system that enables indoor localization and monitoring of older adults living at home or in residential buildings. Hardware consists of custom developed luminaires with sensing, communication and processing capabilities. They replace the existing lighting infrastructure, do not look out of place and are cost effective. The luminaires record the strength of a Bluetooth signal emitted by a wearable device equipped by the monitored user. The system's software server uses trilateration to calculate the person's location based on known luminaire placement and recorded signal strengths. However, multipath fading caused by the presence of walls, furniture and other objects introduces localisation errors. Our previous experiments showed that room-level accuracy can be achieved using software-based filtering for a stationary subject. Our current objective is to assess system accuracy in the context of a moving subject, and ascertain whether room-level localization is feasible in real time

Memory Management Strategies for an Internet of Things System

The rise of the Internet has brought about significant changes in our lives, and the rapid expansion of the Internet of Things (IoT) is poised to have an even more substantial impact by connecting a wide range of devices across various application domains. IoT devices, especially low-end ones, are constrained by limited memory and processing capabilities, necessitating efficient memory management within IoT operating systems. This paper delves into the importance of memory management in IoT systems, with a primary focus on the design and configuration of such systems, as well as the scalability and performance of scene management. Effective memory management is critical for optimizing resource usage, responsiveness, and adaptability as the IoT ecosystem continues to grow. The study offers insights into memory allocation, scene execution, memory reduction, and system scalability within the context of an IoT system, ultimately highlighting the vital role that memory management plays in facilitating a seamless and efficient IoT experience.Comment: International Symposium on Fundamentals of Electrical Engineering 202

Smart Home Environment Modelled with a Multi-Agent System

A smart home can be considered a place of residence that enables the management of appliances and systems to help with day-to-day life by automated technology. In the current paper is described a prototype that simulates a context-aware environment, developed in a designed smart home. The smart home environment has been simulated using three agents and five locations in a house. The context-aware agents behave based on predefined rules designed for daily activities. Our proposal aims to reduce operational cost of running devices. In the future, monitors of health aspects belonging to home residents will sustain their healthy life daily.Comment: 12 pages, 8 figures, journal articl

PIG WELFARE THROUGH BEHAVIOR LEARNING FROM CAMERA RECORDINGS

Animal Science students along with the farm staff have to monitor the behavior of pigs in order to assure their welfare. The video systems are used by our educational software and new methods of pig observation, evaluation and treatment are applied much faster and more efficient compared to the classical intervention. Each recording is stored as a media file and each frame taken at 0.1 seconds is stored as a Bitmap image. The Bitmap images are processed in parallel using the MapReduce programming model from Apache Hadoop. The contour of the image is automatically analyzed and based on it the presence of pigs is detected, as well as their location can be determined. The location is important because it can be denoting that the pig eats or that it stays aside. Pig limp was also detected. It was observed based on the recordings that 83% of the time the pigs spend it lying down, 7% is spent eating and 10% of the time they walk and sit. Video monitoring and automatic interpretation facilitates the learning of new intervention approaches and boosts the responsiveness among the students. The students can learn from the critical situations and benefit from these cases while learning

New digital skills training approach to enhance life quality / Nova abordagem de treinamento de habilidades digitais para melhorar a qualidade de vida

The article outlines the need for professional services and abilities in the healthcare sector. The Erasmus+ DIGI4ME project intends to offer digital skills trainings for healthcare professionals. An investigation of the Romanian health information system was directed in the view of the National Public Health Institute of Romania interviews with stakeholders. Based on the accidents which took place in clinics all around the world, a framework based on sensors is proposed to alleviate the issues which can show up. The testing of the framework was performed in a research center and the reports show the advancement of the monitored environmental boundaries. The software solution can help clinical experts to upgrade their digital abilities and trigger imaginative ideas to enhance the experience of their patients

AUTOMATIC RECOGNITION OF DENTAL PATHOLOGIES AS PART OF A CLINICAL DECISION SUPPORT PLATFORM

The current work is done within the context of Romanian National Program II (PNII) research project "Application for Using Image Data Mining and 3D Modeling in Dental Screening" (AIMMS). The AIMMS project aims to design a program that can detect anatomical information and possible pathological formations from a collection of digital imaging and communications in medicine (DICOM) images. The main function of the AIMMS platform is to provide the user with the opportunity to use an integrated dental support platform, using image processing techniques and 3D modeling. From the literature review, it can be found that for the detection and classification of teeth and dental pathologies existing studies are in their infancy. Therefore, the work reported in this article makes a scientific contribution in this field. In this article it is presented the relevant literature review and algorithms that were created for detection of dental pathologies in the context of research project AIMMS