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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Monitoring the technical state of an urban drainage (UD) system is at the core of asset management, the deployment of visual inspection technology (either using direct visual access for inspection of applying photo and/or video cameras) was and has remained the main method of gathering information on the technical state. Despite some known fundamental shortcomings visual inspection is expected to remain the main source of information for inspection for the foreseeable future. This chapter discusses the virtues of visual inspection but also provides insight into other technologies that have been tried and/or deployed on a more limited scale but do offer access to more and more exact information when compared to the visual methods. Although not much experience is available, inspection techniques for nature-based solutions will be discussed as well
While smart maintenance is gaining popularity in professional engineering and construction management practice, little is known about the dimensions of its maturity. It is assumed that the complex networked environment of maintenance and the rise of data-driven methodologies require a different perspective on maintenance. This paper identifies maturity dimensions for smart maintenance of constructed assets that can be measured. A research design based on two opposite cases is used and data from multiple sources is collected in four embedded case studies in corporate facility management organizations. Through coding data in several cross-case analyses, a maturity framework is designed that is validated through expert consultation. The proposed smart maintenance maturity framework includes technological dimensions (e.g., tracking and tracing) as well as behavioral dimensions (e.g., culture). It presents a new and encompassing theoretical perspective on client leadership in digital construction, integrating innovation in both construction and maintenance supply networks.
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.
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.
In dit project ontwikkelen we Herstelcirkel ++, een gezondheidscoöperatie voor mensen met (risico op) leefstijlgerelateerde aandoeningen (o.a. diabetes). Coöperatie definiëren wij open als een (maatschappelijke) onderneming of autonome organisatie waarbij de deelnemers zeggenschap hebben over hoe zij voorzien in hun behoeften door het realiseren of beheren van voorzieningen en/of diensten. Deze ontwikkeling beantwoordt de wens van mensen met leefstijlaandoeningen (meer regie over de eigen gezondheid) en de door professionals gevoelde noodzaak om de eerstelijnszorg toegankelijk te houden. Het uitgangspunt is dat zorg en gezondheidsbevordering zoveel mogelijk rond, door en voor mensen met vergelijkbare wensen georganiseerd kan worden, in de eigen omgeving zodat de stap naar formele zorg minder nodig is. Complementair aan formele zorg en duurzaam verankerd in een wijklandschap van gezondheidsbevordering. Ondanks Nederlandse burgerinitiatieven rond zorg en gezondheid ontstaan coöperatieve vormen van zelfhulp niet altijd en overal, vooral niet in stadswijken (met achterstandsproblematiek). Hoe kunnen professionals die in de wijk actief zijn rond zorg, gezondheid en welzijn en MKB-bedrijven die zoeken naar innovatieve dienstverlening m.b.t. voeding, beweging en coaching samen met bewoners meer coöperatieve samenwerking bewerkstelligen ten behoeve van vitaliteit? Centraal in dit project staat de doorontwikkeling van Herstelcirkel in de wijk (HCIW) een sociale innovatie die diabetes-zelfmanagementeducatie en zelfhulp combineert door groepen mensen onder begeleiding van coaching aan leefstijlverandering te laten werken. Ondanks veelbelovende resultaten na het eenjarige traject, blijkt voor het merendeel het effect niet duurzaam. Uitgangspunten project: Co-designaanpak die professionals leert kennismaken met ontwerpgerichte methoden om met en voor bewoners passende dienstverlening in de wijk te ontwikkelen die coöperatieve zelfhulp faciliteren. Versterken van positieve krachten van bewoners en wijken (‘assets’) als elementen van de sociale en fysieke leefomgeving die deelnemers in staat stellen gezondheid te bevorderen. Ontwikkeling van een coöperatie, inclusief organisatorische aspecten: samenwerking met gezondheids- en welzijnsprofessionals en duurzame verankering in de wijk.
