7,561 research outputs found

    Public administration management and social services in Czech municipalities: Perceived attitudes of municipal officials with the potential application of the smart city principles

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    Increasing demand for social services connected to the demographic and other changes of the European society presents serious challenge for public policy makers and for the administrative levels most concerned with social services provision. In the Czech Republic this level is represented by municipalities whose attitudes towards the offer of social services in their territory, the providers and the cooperation in this field were surveyed in this paper. Municipal officials’ attitudes to social services differ significantly within the researched sample from a full satisfaction with the present situation in a municipality to a strong dissatisfaction. The paper uses the results of the survey proceed by means of the principal component analysis and cluster analysis to identify the attitudes of the municipal officials in the Zlín Region of the Czech Republic and to divide said municipalities into four groups with similar attitudes and issues for which common policy measures may be adopted after more detailed inquiries into their specific issues. Close monitoring, as provided by this paper, presents detailed basis for local policy makers and the further planning of social services network. © 2017, Academy of Economic Studies from Bucharest. All rights reserved

    The Internet of Things Will Thrive by 2025

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    This report is the latest research report in a sustained effort throughout 2014 by the Pew Research Center Internet Project to mark the 25th anniversary of the creation of the World Wide Web by Sir Tim Berners-LeeThis current report is an analysis of opinions about the likely expansion of the Internet of Things (sometimes called the Cloud of Things), a catchall phrase for the array of devices, appliances, vehicles, wearable material, and sensor-laden parts of the environment that connect to each other and feed data back and forth. It covers the over 1,600 responses that were offered specifically about our question about where the Internet of Things would stand by the year 2025. The report is the next in a series of eight Pew Research and Elon University analyses to be issued this year in which experts will share their expectations about the future of such things as privacy, cybersecurity, and net neutrality. It includes some of the best and most provocative of the predictions survey respondents made when specifically asked to share their views about the evolution of embedded and wearable computing and the Internet of Things

    Design and evaluation of a smart home voice interface for the elderly ― Acceptability and objection aspects

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    Impact-F=1.13 estim. in 2012International audienceSmart homes equipped with ambient intelligence technology constitute a promising direction to enable the growing number of elderly to continue to live in their own home as long as possible. However, this calls for technological solutions that suit their specific needs and capabilities. The SWEET-HOME project aims at developing a new user friendly technology for home automation based on voice command. This paper reports a user evaluation assessing the acceptance and fear of this new technology. Eight healthy persons between 71 and 88 years old, 7 relatives (child, grandchild or friend) and 3 professional carers participated in a user evaluation. During about 45 min, the persons were questioned in co-discovery in the DOMUS smart home alternating between interview and wizard of Oz periods followed by a debriefing. The experience aimed at testing four important aspects of the project: voice command, communication with the outside world, domotics system interrupting a person's activity, and electronic agenda. Voice interface appeared to have a great potential to ease daily living for elderly and frail persons and would be better accepted than more intrusive solutions. By considering still healthy and independent elderly people in the user evaluation, an interesting finding that came up is their overall acceptance provided the system does not drive them to a lazy lifestyle by taking control of everything. This particular fear must be addressed for the development of smart homes that support daily living by giving them more ability to control rather than putting them away from the daily routine

    LSDA responds: towards a unified e-learning strategy

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    [Subject benchmark statement]: computing

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    Unobtrusive Health Monitoring in Private Spaces: The Smart Home

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    With the advances in sensor technology, big data, and artificial intelligence, unobtrusive in-home health monitoring has been a research focus for decades. Following up our research on smart vehicles, within the framework of unobtrusive health monitoring in private spaces, this work attempts to provide a guide to current sensor technology for unobtrusive in-home monitoring by a literature review of the state of the art and to answer, in particular, the questions: (1) What types of sensors can be used for unobtrusive in-home health data acquisition? (2) Where should the sensors be placed? (3) What data can be monitored in a smart home? (4) How can the obtained data support the monitoring functions? We conducted a retrospective literature review and summarized the state-of-the-art research on leveraging sensor technology for unobtrusive in-home health monitoring. For structured analysis, we developed a four-category terminology (location, unobtrusive sensor, data, and monitoring functions). We acquired 912 unique articles from four relevant databases (ACM Digital Lib, IEEE Xplore, PubMed, and Scopus) and screened them for relevance, resulting in n=55 papers analyzed in a structured manner using the terminology. The results delivered 25 types of sensors (motion sensor, contact sensor, pressure sensor, electrical current sensor, etc.) that can be deployed within rooms, static facilities, or electric appliances in an ambient way. While behavioral data (e.g., presence (n=38), time spent on activities (n=18)) can be acquired effortlessly, physiological parameters (e.g., heart rate, respiratory rate) are measurable on a limited scale (n=5). Behavioral data contribute to functional monitoring. Emergency monitoring can be built up on behavioral and environmental data. Acquired physiological parameters allow reasonable monitoring of physiological functions to a limited extent. Environmental data and behavioral data also detect safety and security abnormalities. Social interaction monitoring relies mainly on direct monitoring of tools of communication (smartphone; computer). In summary, convincing proof of a clear effect of these monitoring functions on clinical outcome with a large sample size and long-term monitoring is still lacking
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