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47 results for “Smart Homes”
Smart Home Care of Cloud Base ECG on the Cardiotoxicity Prevention on the Cancer Patients.
ClinicalTrials.gov study NCT04885088. IPD Sharing: NO. Countries: 0. Publications: 0.
Feasibility of a Smart Device Application for Home-based Prehabilitation
ClinicalTrials.gov study NCT05363150. IPD Sharing: UNDECIDED. Countries: 0. Publications: 0.
Using SMART HOME Strategy to Reduce the Incidence of Delirium in the Intensive Care Unit
ClinicalTrials.gov study NCT06030453. IPD Sharing: Not stated. Countries: 0. Publications: 0.
eCAPE smart home interviews
<p>This dataset contains transcripts from semi-structured interviews with 15 Danish households with smart home technology installed. The interviews were conducted in 2020 as part of the eCAPE project (New energy consumer roles and smart technologies – actors, practices and equality) running from 2018-2023. eCAPE is financed by the European Research Council (ERC) under the European Union´s Horizon 2020 research and innovation program (grant agreement number 786643). The project is led by Professor Kirsten Gram-Hanssen from Department of the Built Environment, Aalborg University, and the interviews were conducted by Line Kryger Aagaard from Department of the Built Environment, Aalborg University, as part of her PhD study within eCAPE. Interviews were transcribed by Line Kryger Aagaard and a student assistant.</p> <p>The participating households had a combination of various smart home technologies (e.g. smart lighting, smart heating, digital voice assistants, robotic vacuum cleaners). The recruitment process is described in a paper by Aagaard & Madsen (2022): “The first nine households in the study were recruited via SHT [smart home technology] Facebook groups where people share experiences and advice, and six additional households were recruited via snowball sampling referred from the initial participants and from one contact of the authors. Only men responded to the posted research call and common to all of them was an outspoken interest in technology. They all lived in opposite-sex relationships, except one single man, and were asked to bring their female partners for the interviews, which 12 out of 14 did” (Aagaard & Madsen 2022: 680).</p> <p>All names are pseudonyms and participants’ age, occupation and other personal data have in some cases been altered and in some cases paragraphs have been removed, all to ensure anonymity. Alterations are marked with bold writing and removed parts are indicated with brackets and X’s. Participants received written and oral information on the research purpose and handling of their personal data and gave their written consent to participate. The interviews were conducted in the participants’ homes, except from one interview that was conducted online. Photos were taken during the interviews with participants’ consent, but these photos have not been published due to the protection of the anonymity of participants. </p> <p>Apart from the interview transcripts this dataset contains the interview guide (Danish and English version) and the posted call for research participants (Danish and English version).</p> <p>To this date, the interview data have been analyzed in two journals papers:</p> <ul> <li>Aagaard, Line Kryger. 2022. ‘When Smart Technologies Enter Household Practices: The Gendered Implications of Digital Housekeeping’. <em>Housing, Theory and Society</em> 0 (0): 1–18. <a href="https://doi.org/10.1080/14036096.2022.2094460">https://doi.org/10.1080/14036096.2022.2094460</a>.</li> <li>Aagaard, Line Kryger, and Line Valdorff Madsen. 2022. ‘Technological Fascination and Reluctance: Gendered Practices in the Smart Home’. <em>Buildings and Cities</em> 3 (1): 677–91. <a href="https://doi.org/10.5334/bc.205">https://doi.org/10.5334/bc.205</a>.</li> </ul>
Groceries Tracking System - Smart Homes
<p>It's a sample data set of home groceries data set used for "Vision-Based Automatic Groceries Tracking System - Smart Homes" project. It contains labeled home groceries shelving, store groceries shelving, and groceries pictures. </p>
Fuzzy Spatiotemporal Data Mining to Activity Recognition in Smart Homes
A primary goal to design smart homes is to provide automatic assistance for the residents to make them able to live independently at home. Activity recognition is done to achieve the mentioned goal and then to provide assistance, we would need three sort of information. First, we would need to know the goal of the resident, then the pattern that the resident should obey to achieve its goal and third sort of needed information is the deviations from the previously known patterns. In the presented paper, spatiotemporal aspects of daily activities are surveyed to mine the patterns of activities realized by the smart homes residents. Necessary data to model the spatiotemporal aspects of daily activities is provided by embedded sensors in the smart home. We believe that to accomplish daily activities, specific objects are applied and by analyzing the movement of objects and resident(s), we would obtain valuable information to model the daily activities of the Smart Home’s residents.
Activity modeling under uncertainty by trace of objects in smart homes
A typical resident of a smart home can be an Alzheimer patient that forgets sometimes to complete the activities that he begins. The key point to assist the smart home resident is to model the activities and discover correct realization patterns of activities. To accomplish this task, we apply sensors to provide primary data about realization patterns of actions, operations, plans, goals and generally any objective that the smart home resident may desire to do. In the consequence, by applying fuzzy clustering techniques, we are able to mine sensor data to retrieve the realization patterns of activities, and so the prediction patterns of intentions are recognizable. Comparing the realization patterns with prediction patterns of activities, we would be able to predict the intention of the resident about the activity that the resident considers to realize. In this way, we would be able to provide hypotheses about the resident goals and his possible goal achievement’s defects. Spatiotemporal aspects of daily activities such as movement of objects are surveyed to discover the patterns of activities realized by the smart homes residents. In this research, uncertainty is considered as a property of activity recognition.
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.