ZoekresultatenZoekresultaten
Volg ons op
Volg ons op
Types
0Instelling
26Publicatiejaar
6Producttype
19Bestandstype
9Taal
5Onderzoeksthema's
41Publicaties met bestand/URL
2Projectstatus
3This project investigates the added value of sensor based technology in district nursing.
DOCUMENT
Purpose People with dementia (PwD) often present Behavioral and Psychological Symptoms of Dementia, which include agitation, apathy, and wandering amongst others, also known as challenging behaviors (CBs). These CBs worsen the quality of life (QoL) of the PwD and are a major source/reason of (increased) caregiver burden. The intricate nature of the symptoms implies that there is no “one size fits all solution”, and necessitates tailored approaches for both PwDs and caregivers. To timely prevent these behaviors assistive technology can be utilized to guide caregivers by enabling remote monitoring of contextual, environmental, and behavioral parameters, and subsequently alarming nurses on early-stage behavioral changes prior to the presentation of CBs. Eventually, the system should propose an intervention/action to prevent escalation. In turn, improvement in QoL for both caregivers and PwD living in nursing homes (NHs) is expected. In the current project “MOnitoring Onbegrepen Gedrag bij Dementie met sensortechnologie” (MOOD-Sense), we aim to develop such a monitoring system. The strengths of this new monitoring system lie in its ability to align with the individual needs of the PwD, utilization of a combination of wearables and ambient sensors to obtain contextual data, such as location or sound, and predict or monitor CBs individually rather than in groups, thus facilitating person-centered care, based on ontological reasoning. The project is divided into three parts, Toolbox A, B and C. Toolbox A focuses on obtaining insight in which behaviors are challenging according to nurses and how they are described. Previous studies utilize clinical terminology to describe or classify behavior, we aim to employ concrete descriptions of behavior that are observable and independent of clinical terminology, aligning with nurses who are often the first to notice behavior and can be operationalized such that it can also be aligned with sensor data. As a result, an ontology will be developed based on the data such that sensor data can be integrated into the same conceptual information that standardizes the communication in our monitoring system. Toolbox B focuses on translating data coming from various sensors into the concepts expressed in the ontology, and timely communicate situations of interest to the caregivers. In Toolbox C the focus is exploring interventions/actions employed in practice to prevent CBs. Method In Toolbox A we used a qualitative approach to collect descriptions of CBs. For this purpose, we employed focus groups (FGs) with nursing staff who provide daily care to PwD. In Toolbox B pilot studies were conducted. A set of experiments using sensors in NHs were performed. During each pilot, multiple PwD with CBs in NHs were monitored with both ambient and wearables sensors. The pilots were iteratively approached, which means that insights from previous pilot studies were used to improve consecutive pilot studies. Lastly, the elaboration of Toolbox C is ongoing. Results and Discussion Regarding Toolbox A four FGs were conducted during the period from January 2023 to May 2024. Each FG was comprised of four nurses (n = 16). From the FGs we gained insights into behavioral descriptions and the context of CBs. Although data analysis has to be performed yet, there are indications that changes preceding CBs can be observed, such as frowning or clenching fists for agitation or aggression. Further results will be available soon. Regarding Toolbox B a monitoring system, based on sensors, is developed iteratively (see Figure 1) and piloted in three consecutive NHs from January 2021 to December 2023. Each pilot was comprised of two PwD (n = 6). Analysis of sensor data is ongoing.
DOCUMENT
The creation of artifacts is one of the factors that make us human. Artifacts contribute to our continual adaptation to the world by permitting better knowledge and control of it. The focus of this chapter is on the role of one specific kind of artifact: sensors. In contrast to our immediate perception of the world from our senses, sensors provide large amount of reliable measurements of the physical world that enhance human cognitive capacities in overcoming our perceptual limitations. However, “raw” sensor data require interpretation that relies on different types of expertise and knowledge to provide relevant meaning for human (adaptive) purposes. We suggest that a cognitive approach to understanding the differences between the different types of knowledge provided by current sensors as artifacts and the human senses is of interest. This approach questions the conception of human cognition as an analytic system of processing information from the world rather than as one which interprets and gives meanings to the world. We posit that understanding the differences between human and artificial sensors can shape a new era of technological advancement that is uniquely collaborative insofar as it would rely on the partnership of scientists working in the Humanities and in the Natural Sciences. In this article we provide some data from cognitive research that outline the beginnings of a pluridisciplinary endeavor to conceive sensors which integrate performances of artifacts and the diversity and richness of human cognition, with the goal of transforming so-called “intelligent” devices into cognitive sensors.
DOCUMENT
This paper describes the approach used to identify elderly people’s needs and attitudes towards applying ambient sensor systems for monitoring daily activities in the home. As elderly are typically unfamiliar with such ambient technology, interactive tools for explicating sensor monitoring –an interactive dollhouse and iPad applications for displaying live monitored sensor activity data– were developed and used for this study. Furthermore, four studies conducted by occupational therapists with more than 60 elderly participants –including questionnaires (n=41), interviews (n=6), user sessions (n=14) and field studies (n=2)– were conducted. The experiences from these studies suggest that this approach helped to democratically engage the elderly as end-user and identify acceptance issues.
DOCUMENT
Technology is becoming omnipresent in public spaces: from CCTV cameras to smart phones, and from large public displays to RFID enabled travel cards. Although such technology comes with great potential, it also comes with apparent (privacy) threats and acceptance issues. Our research focuses on realizing technologyenhanced public spaces in a way that is acceptable and useful for the public. This paper gives a brief overview of the research that is aimed to unlock the positive potential of public spaces. This paper’s main focus is on the acceptance of sensor technology in the realm of tourism. The ITour project which investigates the potential and acceptance of using (sensor) technology and ambient media to collect, uncover and interpret data regarding tourists’ movements, behavior and experiences in the city of Amsterdam is particularly discussed as an example.
DOCUMENT
An energy harvesting device for obtaining energy from drops without needing of moving the drops along the device, in a reduced scale and combinable with other
types of harvesting devices, the energy harvesting device comprising one or more triboelectric generators comprising a bottom electrode, a friction or triboelectric element placed over the bottom electrode, and at least two top electrodes placed over the triboelectric element and defining at least one gap between them, exposing the triboelectric element to the external environment so that on contacting a drop of liquid makes an electrical connection between the top electrodes varying the capacitance of the triboelectric generators and alternatively for functioning as a power unit for a sensor or as a self-powered sensor producing an electrical signal generated by the contact of the liquid with the electrodes.
DOCUMENT
In the GoGreen project an intelligent home that is able to identify inhabitants and events that take place is created. The location of sounds that are being produced is an important feature for the context awareness of this system. A a wireless solution that uses low-cost sensor nodes and microphones is described. Experiments show that solutions that only use the three sensor nodes that are closest to the origin of the sounds provide the best solutions, with an average accuracy of 40 cm or less.Paper published for the ICT Open 2013 proceedings (27-28 November 2013, Eindhoven).
DOCUMENT
Sensor technology is increasingly applied for the purpose of monitoring elderly’s Activities of Daily Living (ADL), a set of activities used by physicians to benchmark physical and cognitive decline. Visualizing deviations in ADL can help medical specialists and nurses to recognize disease symptoms at an early stage. This paper presents possible visualizations for identifying such deviations. These visualizations have been iteratively explored and developed with three different medical specialists to better understand which deviations are relevant according to the different medical specialisms and explore how these deviations should be best presented. The study results suggest that the participants found a monthly bar graph in which activities are represented by colours as the most suitable from the ones presented. Although the visualizations of every ADL was found to be more or less relevant by the different medical specialists, the preference for focusing on specific ADL’s varied from specialist to specialist.
DOCUMENT
A novel type of application for the exploration of enclosed or otherwise difficult to access environments requires large quantities of miniaturized sensor nodes to perform measurements while they traverse the environment in a “go with the flow” approach. Examples of these are the exploration of underground cavities and the inspection of industrial pipelines or mixing tanks, all of which have in common that the environments are difficult to access and do not allow position determination using e.g. GPS or similar techniques. The sensor nodes need to be scaled down towards the millimetre range in order to physically fit through the narrowest of parts in the environments and should measure distances between each other in order to enable the reconstruction of their positions relative to each other in offline analysis. Reaching those levels of miniaturization and enabling reconstruction functionality requires: 1) novel reconstruction algorithms that can deal with the specific measurement limitations and imperfections of millimetre-sized nodes, and 2) improved understanding of the relation between the highly constraint hardware design space of the sensor nodes and the reconstruction algorithms. To this end, this work provides a novel and highly robust sensor swarm reconstruction algorithm and studies the effect of hardware design trade-offs on its performance. Our findings based on extensive simulations, which push the reconstruction algorithm to its breaking point, provide important guidelines for the future development of millimetre-sized sensor nodes.
DOCUMENT
The maturing field of Wireless Sensor Networks (WSN) results in long-lived deployments that produce large amounts of sensor data. Lightweight online on-mote processing may improve the usage of their limited resources, such as energy, by transmitting only unexpected sensor data (anomalies). We detect anomalies by analyzing sensor reading predictions from a linear model. We use Recursive Least Squares (RLS) to estimate the model parameters, because for large datasets the standard Linear Least Squares Estimation (LLSE) is not resource friendly. We evaluate the use of fixed-point RLS with adaptive thresholding, and its application to anomaly detection in embedded systems. We present an extensive experimental campaign on generated and real-world datasets, with floating-point RLS, LLSE, and a rule-based method as benchmarks. The methods are evaluated on prediction accuracy of the models, and on detection of anomalies, which are injected in the generated dataset. The experimental results show that the proposed algorithm is comparable, in terms of prediction accuracy and detection performance, to the other LS methods. However, fixed-point RLS is efficiently implementable in embedded devices. The presented method enables online on-mote anomaly detection with results comparable to offline LS methods. © 2013 IEEE.
DOCUMENT
