Purpose – In the domain of healthcare, both process efficiency and the quality of care can be improved through the use of dedicated pervasive technologies. Among these applications are so-called real-time location systems (RTLS). Such systems are designed to determine and monitor the location of assets and people in real time through the use of wireless sensor networks. Numerous commercially available RTLS are used in hospital settings. The nursing home is a relatively unexplored context for the application of RTLS and offers opportunities and challenges for future applications. The paper aims to discuss these issues. Design/methodology/approach – This paper sets out to provide an overview of general applications and technologies of RTLS. Thereafter, it describes the specific healthcare applications of RTLS, including asset tracking, patient tracking and personnel tracking. These overviews are followed by a forecast of the implementation of RTLS in nursing homes in terms of opportunities and challenges. Findings – By comparing the nursing home to the hospital, the RTLS applications for the nursing home context that are most promising are asset tracking of expensive goods owned by the nursing home in orderto facilitate workflow and maximise financial resources, and asset tracking of personal belongings that may get lost due to dementia. Originality/value – This paper is the first to provide an overview of potential application of RTLS technologies for nursing homes. The paper described a number of potential problem areas that can be addressed by RTLS. Published by Emerald Publishing Limited Original article: https://doi.org/10.1108/JET-11-2017-0046 For this paper Joost van Hoof received the Highly Recommended Award from Emerald Publishing Ltd. in October 2019: https://www.emeraldgrouppublishing.com/authors/literati/awards.htm?year=2019
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Real-time location systems (RTLS) can be implemented in aged care for monitoring persons with wandering behaviour and asset management. RTLS can help retrieve personal items and assistive technologies that when lost or misplaced may have serious financial, economic and practical implications. Various ethical questions arise during the design and implementation phases of RTLS. This study investigates the perspectives of various stakeholders on ethical questions regarding the use of RTLS for asset management in nursing homes. Three focus group sessions were conducted concerning the needs and wishes of (1) care professionals; (2) residents and their relatives; and (3) researchers and representatives of small and medium-sized enterprises (SMEs). The sessions were transcribed and analysed through a process of open, axial and selective coding. Ethical perspectives concerned the design of the system, the possibilities and functionalities of tracking, monitoring in general and the user-friendliness of the system. In addition, ethical concerns were expressed about security and responsibilities. The ethical perspectives differed per focus group. Aspects of privacy, the benefit of reduced search times, trust, responsibility, security and well-being were raised. The main focus of the carers and residents was on a reduced burden and privacy, whereas the SMEs stressed the potential for improving products and services. Original article at MDPI: https://doi.org/10.3390/info9040080
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In this paper, the performance gain obtained by combining parallel peri- odic real-time processes is elaborated. In certain single-core mono-processor configurations, for example, embedded control systems in robotics comprising many short processes, process context switches may consume a considerable amount of the available processing power. For this reason, it can be advantageous to combine processes, to reduce the number of context switches and thereby increase the performance of the application. As we consider robotic applications only, often consisting of processes with identical periods, release times and deadlines, we restrict these configurations to periodic real-time processes executing on a single-core mono-processor. By graph-theoretical concepts and means, we provide necessary and sufficient conditions so that the number of context switches can be reduced by combining synchronising processes.
The utilization of drones in various industries, such as agriculture, infrastructure inspection, and surveillance, has significantly increased in recent years. However, navigating low-altitude environments poses a challenge due to potential collisions with “unseen” obstacles like power lines and poles, leading to safety concerns and equipment damage. Traditional obstacle avoidance systems often struggle with detecting thin and transparent obstacles, making them ill-suited for scenarios involving power lines, which are essential yet difficult to perceive visually. Together with partners that are active in logistics and safety and security domains, this project proposal aims at conducting feasibility study on advanced obstacle detection and avoidance system for low-flying drones. To that end, the main research question is, “How can AI-enabled, robust and module invisible obstacle avoidance technology can be developed for low-flying drones? During this feasibility study, cutting-edge sensor technologies, such as LiDAR, radar, camera and advanced machine learning algorithms will be investigated to what extent they can be used be to accurately detect “Not easily seen” obstacles in real-time. The successful conclusion of this project will lead to a bigger project that aims to contribute to the advancement of drone safety and operational capabilities in low-altitude environments, opening new possibilities for applications in industries where low-flying drones and obstacle avoidance are critical.
Real-Time Cyber-Physical Systems (RT-CPS) zijn onmisbaar in onze samenleving, van medische apparatuur tot autonome voertuigen. De betrouwbaarheid en robuustheid van deze systemen zijn echter cruciaal, fouten kunnen immers grote gevolgen hebben. Dit project beoogt de betrouwbaarheid van RT-CPS te vergroten door middel van een modulaire hardware-architectuur en geavanceerde validatie- en verificatiemethoden (V&V). In samenwerking met praktijkpartners, waaronder het Wilhelmina Kinderziekenhuis, wordt een proof-of-concept demonstrator ontwikkeld in een praktijkgerichte casus. De modulaire hardware-architectuur maakt RT-CPS flexibeler, toekomstbestendig en breed toepasbaar. De geavanceerde V&V-methoden borgen de betrouwbaarheid van de systemen en helpen MKB-bedrijven bij de ontwikkeling van hun eigen RT-CPS-applicaties. Naast de directe voordelen voor de betrokken partners, draagt dit project bij aan een bredere maatschappelijke impact. De verhoogde betrouwbaarheid van RT-CPS kan leiden tot verbeterde veiligheid en efficiëntie in diverse sectoren. Een krachtige samenwerking tussen kennisinstituten, praktijkpartners en het MKB is de sleutel tot succes. Dit project bundelt expertise en praktijkkennis om Nederland een leidende positie te laten innemen op het gebied van betrouwbare RT-CPS. In dit 1-jarig verkennend project zal de Hogeschool van Arnhem en Nijmegen samenwerken met Gemini Embedded Technology, Wilhelmina Kinderziekenhuis, het grootbedrijf Capgemini en de Universiteit Utrecht.
The bi-directional communication link with the physical system is one of the main distinguishing features of the Digital Twin paradigm. This continuous flow of data and information, along its entire life cycle, is what makes a Digital Twin a dynamic and evolving entity and not merely a high-fidelity copy. There is an increasing realisation of the importance of a well functioning digital twin in critical infrastructures, such as water networks. Configuration of water network assets, such as valves, pumps, boosters and reservoirs, must be carefully managed and the water flows rerouted, often manually, which is a slow and costly process. The state of the art water management systems assume a relatively static physical model that requires manual corrections. Any change in the network conditions or topology due to degraded control mechanisms, ongoing maintenance, or changes in the external context situation, such as a heat wave, makes the existing model diverge from the reality. Our project proposes a unique approach to real-time monitoring of the water network that can handle automated changes of the model, based on the measured discrepancy of the model with the obtained IoT sensor data. We aim at an evolutionary approach that can apply detected changes to the model and update it in real-time without the need for any additional model validation and calibration. The state of the art deep learning algorithms will be applied to create a machine-learning data-driven simulation of the water network system. Moreover, unlike most research that is focused on detection of network problems and sensor faults, we will investigate the possibility of making a step further and continue using the degraded network and malfunctioning sensors until the maintenance and repairs can take place, which can take a long time. We will create a formal model and analyse the effect on data readings of different malfunctions, to construct a mitigating mechanism that is tailor-made for each malfunction type and allows to continue using the data, albeit in a limited capacity.
