Ambient activity monitoring systems produce large amounts of data, which can be used for health monitoring. The problem is that patterns in this data reflecting health status are not identified yet. In this paper the possibility is explored of predicting the functional health status (the motor score of AMPS = Assessment of Motor and Process Skills) of a person from data of binary ambient sensors. Data is collected of five independently living elderly people. Based on expert knowledge, features are extracted from the sensor data and several subsets are selected. We use standard linear regression and Gaussian processes for mapping the features to the functional status and predict the status of a test person using a leave-oneperson-out cross validation. The results show that Gaussian processes perform better than the linear regression model, and that both models perform better with the basic feature set than with location or transition based features. Some suggestions are provided for better feature extraction and selection for the purpose of health monitoring. These results indicate that automated functional health assessment is possible, but some challenges lie ahead. The most important challenge is eliciting expert knowledge and translating that into quantifiable features.
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Existing research on the recognition of Activities of Daily Living (ADL) from simple sensor networks assumes that only a single person is present in the home. In real life there will be situations where the inhabitant receives visits from family members or professional health care givers. In such cases activity recognition is unreliable. In this paper, we investigate the problem of detecting multiple persons in an environment equipped with a sensor network consisting of binary sensors. We conduct a real-life experiment for detection of visits in the oce of the supervisor where the oce is equipped with a video camera to record the ground truth. We collected data during two months and used two models, a Naive Bayes Classier and a Hidden Markov Model for a visitor detection. An evaluation of these two models shows that we achieve an accuracy of 83% with the NBC and an accuracy of 92% with a HMM, respectively.
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Technology has a major impact on the way nurses work. Data-driven technologies, such as artificial intelligence (AI), have particularly strong potential to support nurses in their work. However, their use also introduces ambiguities. An example of such a technology is AI-driven lifestyle monitoring in long-term care for older adults, based on data collected from ambient sensors in an older adult’s home. Designing and implementing this technology in such an intimate setting requires collaboration with nurses experienced in long-term and older adult care. This viewpoint paper emphasizes the need to incorporate nurses and the nursing perspective into every stage of designing, using, and implementing AI-driven lifestyle monitoring in long-term care settings. It is argued that the technology will not replace nurses, but rather act as a new digital colleague, complementing the humane qualities of nurses and seamlessly integrating into nursing workflows. Several advantages of such a collaboration between nurses and technology are highlighted, as are potential risks such as decreased patient empowerment, depersonalization, lack of transparency, and loss of human contact. Finally, practical suggestions are offered to move forward with integrating the digital colleague
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With a market demand for low cost, easy to produce, flexible and portable applications in healthcare, energy, biomedical or electronics markets, large research programs are initiated to develop new technologies to provide this demand with new innovative ideas. One of these fast developing technologies is organic printed electronics. As the term printed electronics implies, functional materials are printed via, e.g. inkjet, flexo or gravure printing techniques, on to a substrate material. Applications are, among others, organic light emitting diodes (OLED), sensors and Lab-on-a-chip devices. For all these applications, in some way, the interaction of fluids with the substrate is of great importance. The most used substrate materials for these low-cost devices are (coated) paper or plastic. Plastic substrates have a relatively low surface energy which frequently leads to poor wetting and/or poor adhesion of the fluids on the substrates during printing and/ or post-processing. Plasma technology has had a long history in treating materials in order to improve wetting or promote adhesion. The µPlasma patterning tool described in this thesis combines a digital inkjet printing platform with an atmospheric dielectric barrier discharge plasma tool. Thus enabling selective and local plasma treatment, at atmospheric pressure, of substrates without the use of any masking materials. In this thesis, we show that dependent on the gas composition the substrate surface can either be functionalized, thus increasing its surface energy, or material can be deposited on the surface, lowering its surface energy. Through XPS and ATR-FTIR analysis of the treated (polymer) substrate surfaces, chemical modification of the surface structure was confirmed. The chemical modification and wetting properties of the treated substrates remained present for at least one month after storage. Localized changes in wettability through µPlasma patterning were obtained with a resolution of 300µm. Next to the control of wettability of an ink on a substrate in printed electronics is the interaction of ink droplets with themselves of importance. In printing applications, coalescence of droplets is standard practice as consecutive droplets are printed onto, or close to each other. Understanding the behaviour of these droplets upon coalescence is therefore important, especially when the ink droplets are of different composition and/or volume. For droplets of equal volume, it was found that dye transport across the coalescence bridge could be fully described by diffusion only. This is as expected, as due to the droplet symmetry on either side of the bridge, the convective flows towards the bridge are of equal size but opposite in direction. For droplets of unequal volume, the symmetry across the bridge is no longer present. Experimental analysis of these merging droplets show that in the early stages of coalescence a convective flow from the small to large droplet is present. Also, a smaller convective flow of shorter duration from the large into the small droplet was identified. The origin of this flow might be due to the presence of vortices along the interface of the bridge, due to the strong transverse flow to open the bridge. To conclude, three potential applications were showcased. In the first application we used µPlasma patterning to create hydrophilic patterns on hydrophobic dodecyl-trichlorosilane (DTS) covered glass. Capillaries for a Lab-on-a-chip device were successfully created by placing two µPlasma patterned glass slides on top of each other separated by scotch tape. In the second application we showcased the production of a RFID tag via inkjet printing. Functional RFID-tags on paper were created via inkjet printing of silver nanoparticle ink connected to an integrated circuit. The optimal operating frequency of the produced tags is in the range of 860-865 MHz, making them usable for the European market, although the small working range of 1 m needs further improvement. Lastly, we showed the production of a chemresistor based gas sensor. In house synthesised polyemeraldine salt (PANi) was coated by hand on top of inkjet printed silver electrodes. The sensor proved to be equally sensitive to ethanol and water vapour, reducing its selectivity in detecting changes in gas composition.
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The need for care will increase in the coming years. Most people with a disability or old age receive support from an informal caregiver. Caring for a person with dementia can be difficult because of the BPSD (Behavioral and Psychological Symptoms of Dementia). BPSD, including sleep disturbance, is an important factor for a higher care load. In this scoping review, we aim to investigate whether technology is available to support the informal caregiver, to lower the care burden, improve sleep quality, and therefore influence the reduction of social isolation of informal caregivers of people with dementia. A scoping review is performed following the methodological framework by Arksey and O'Mally and Rumrill et al., the scoping review includes scientific and other sources (unpublished literature, websites, reports, etc.). The findings of the scoping review shows that there are technology applications available to support the informal caregiver of a person with dementia. The technology applications mostly contribute to lower the care burden and/or improve sleep quality and therefore may contribute to reduce social isolation. The technology applications found target either the person with dementia, the informal caregiver, or both.
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