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47 results for “Smart Home”
Survey data on households' use of smart home technology and their time of use of electric appliances (eCAPE)
<p>This survey data includes the responsed from a survey questionnaire which was used to collect information on smart home technologies and time of use of electric appliances in Danish households. The survey covers themes like adoption and use ofhousehold appliances, households’ division of everyday chores, timing of everyday activities, and everyday flexibility.</p> <p>The purpose of this survey is to gather information about Danish households and their everyday practices and flexibility related to electricity use. The intention is to combine questions from the survey with real time data of electricity consumption at household level with a time resolution of few minutes, and to do so for a large representative population. However, the electricity consumption is not allowed to share publicly, and therefore not included in this data upload. </p> <p>The survey includes questions of socio-economic factors.</p> <p>The questionnaire was distributed in Danish but was developed in and translated from English because ofinternational cooperation.</p> <p>The survey was developed under the project eCAPE - New Energy Consumer Roles and Smart Technologies– Actors, Practices and Equality. The eCAPE project is financed by the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation program under the grant agreement number 786643 (https://www.ecape.aau.dk/). The project is led by Professor Kirsten Gram-Hanssen from Department of the Built Environment, Aalborg University. <br><em>See also </em> <a href="https://www.researchgate.net/publication/373453718_Survey_questionnaire_on_households'_use_of_smart_home_technology_and_their_time_of_use_of_electric_appliances">https://www.researchgate.net/publication/373453718_Survey_questionnaire_on_households'_use_of_smart_home_technology_and_their_time_of_use_of_electric_appliances</a> </p>
Co-design – Part 1: Workshops to explore current imaginaries behind smart home technologies development and use
<h3>Description</h3> <p>This qualitative dataset is the <strong>first part</strong> of a PhD study on co-designing smart home technologies, and represents the data collected during a series of independent <strong>in-person workshops</strong> with professionals developing smart technology, its early-adopters, and late/non-adopters. The data collected during the subsequent parts of the referred study are also available at Zenodo.</p> <h3> </h3> <h3>Documents from workshop with professionals</h3> <ul> <li><strong>P1_WSP-PRO-TRANSCR_R02.docx</strong> (transcription of the workshop's audio recordings)</li> <li><strong>P1_WSP-PRO-VIS_000 </strong>till _<strong>013.jpg</strong> (participant-generated visual data)</li> </ul> <p> </p> <h3>Documents from workshop with early-adopters</h3> <ul> <li><strong>P1_WSP-EA-TRANSCR_R01.docx</strong> (transcription of the workshop's audio recordings)</li> <li><strong>P1_WSP-EA-VIS_000 </strong>till _<strong>011.jpg</strong> (participant-generated visual data)</li> </ul> <p> </p> <h3>Documents from workshop with late/non-adopters</h3> <ul> <li><strong>P1_WSP-LN-TRANSCR_R00.docx</strong> (transcription of the workshop's audio recordings)</li> <li><strong>P1_WSP-LN-VIS_000 </strong>till _<strong>012.jpg</strong> (participant-generated visual data)</li> </ul> <h3> </h3> <h3>Acknowledgements</h3> <p>This study is part of the GECKO Project (<a href="https://gecko-project.eu/">https://gecko-project.eu/</a>) and has received funding from the European Commission under the Horizon2020 MSCA-ITN-2020 Innovative Training Networks programme, Grant Agreement No 955422 (<a href="https://cordis.europa.eu/project/id/955422">https://cordis.europa.eu/project/id/955422</a>).</p>
Co-design – Part 2: Workshop with professionals, early-adopters, and late/non-adopters to design interventions for a more responsible and just future with smart home technologies
<h3>Description</h3> <p>This qualitative dataset is the <strong>second part</strong> of a PhD study on co-designing smart home technologies, and represents the data collected during a series of two <strong>in-person workshops</strong>: one with professionals developing smart technology and its early-adopters, and a second one with late/non-adopters of smart technology. The first workshop had four groups of participants and the second three groups. The data is divided by each group. The data collected during the previous and subsequent parts of the referred study are also available at Zenodo.</p> <h3> </h3> <h3>Documents from workshop with professionals and early-adopters</h3> <ul> <li><strong>P2_WSP-PA-G1-TRANSCR_R00.docx</strong> (transcription of group 1 audio recordings) <ul> <li><strong>P2_WSP-PA-G1-VIS_000 </strong>to <strong>_005</strong> (participant-generated visual data)</li> </ul> </li> </ul> <ul> <li><strong>P2_WSP-PA-G2-TRANSCR_R00.docx </strong>(transcription of group 2 audio recordings) <ul> <li><strong>P2_WSP-PA-G2-VIS_000 </strong>to<strong> _008</strong> (participant-generated visual data)</li> </ul> </li> </ul> <ul> <li><strong>P2_WSP-PA-G3-TRANSCR_R00.docx </strong>(transcription of group 3 audio recordings) <ul> <li><strong>P2_WSP-PA-G3-VIS_000 </strong>to<strong> _004</strong> (participant-generated visual data)</li> </ul> </li> </ul> <ul> <li><strong>P2_WSP-PA-G4-TRANSCR_R00.docx </strong>(transcription of group 4 audio recordings) <ul> <li><strong>P2_WSP-PA-G4-VIS_000 </strong>to<strong> _002</strong> (participant-generated visual data)</li> </ul> </li> </ul> <p> </p> <h3>Documents from workshop with late/non-adopters</h3> <ul> <li><strong>P2_WSP-LN-G1-TRANSCR_R00</strong> (transcription of group 1 audio recordings) <ul> <li><strong>P2_WSP-LN-G1-VIS_000 </strong>and<strong> _001</strong> (participant-generated visual data)</li> </ul> </li> </ul> <ul> <li><strong>P2_WSP-LN-G2-TRANSCR_R00</strong> (transcription of group 2 audio recordings) <ul> <li><strong>P2_WSP-LN-G2-VIS_000 </strong>to<strong> _003</strong> (participant-generated visual data)</li> </ul> </li> </ul> <ul> <li><strong>P2_WSP-LN-G3-TRANSCR_R00</strong> (transcription of group 3 audio recordings) <ul> <li><strong>P2_WSP-LN-G3-VIS_000 </strong>to<strong> _002</strong> (participant-generated visual data)</li> </ul> </li> </ul> <p> </p> <h3>Acknowledgements</h3> <p>This study is part of the GECKO Project (<a href="https://gecko-project.eu/">https://gecko-project.eu/</a>) and has received funding from the European Commission under the Horizon2020 MSCA-ITN-2020 Innovative Training Networks programme, Grant Agreement No 955422 (<a href="https://cordis.europa.eu/project/id/955422">https://cordis.europa.eu/project/id/955422</a>).</p>
Smart Home Sensor and HVAC Control Dataset from the SHAL Demonsrtator of the Aegis Project
<p>The example dataset was produced within the Smart Home and Assisted Living demonstrator of the AEGIS project. IT contains measurements of indoor temperature and HVAC control actions (ON/OFF status and setpoint values), which were be used to extract comfort profiles.</p>
eSense Smart Home: A PIR-based solo-resident Smart Home dataset
<p>In the evolving landscape of smart homes, there is a rising interest in making homes not just smarter but also more suitable for elderlies, in particular, to mitigate the challenges of ageing problem. This is the main motivation, which sheds light on the importance of creating and testing smart systems. In this situation, access to datasets collected from real-life activities is crucial for coming up with new and better ways to improve the systems and solutions developed for smart homes. The eSense Smart Home dataset is collected from a solo-resident smart home testbed equipped with a network of Passive Infrared (PIR) sensors. The sensors are strategically placed to monitor doorways and living areas, capturing detailed insights into occupant's movements and transitions within the house. The data collection was conducted in two rounds with two different setups for sensor placement in the testbed. This dataset offers a valuable resource for researchers and practitioners interested in understanding user behaviour within a home environment and includes raw sensor readings from different areas of the home and a log representing the time spent in each room. More detailed information regarding data collection and event logs are presented in the document that is included within the dataset files.</p>
Handling of Personal Data by Smart Home Equipment: an Exploratory Analysis in the Context of LGPD
<p>This dataset provides data about an exploratory research that analyzed the Privacy and Security Policies and the Instruction Manuals of 59 home automation equipment for Smart Home in order to verify which personal data was handled and how these documents were providing information about processes performed in personal data. The analysis was conducted with a quantitative approach followed by a qualitative analysis, using content analysis.</p>
Consumer 'Smart Home' Technology Predictions For 2019
<p>Source: <a href="https://www.forbes.com/sites/moorinsights/2019/01/09/consumer-smart-home-technology-predictions-for-2019/#">https://www.forbes.com/sites/moorinsights/2019/01/09/consumer-smart-home-technology-predictions-for-2019</a></p>
Exploring Augmented Reality Privacy Icons for Smart Home Devices and their Effect on Users' Privacy Awareness
<p><strong>Exploring Augmented Reality Privacy Icons for Smart Home Devices and their Effect on Users' Privacy Awareness</strong></p> <p><strong>Authors</strong></p> <p>Kathrin Knutzen, Florian Weidner, Wolfgang Broll</p> <p> </p> <p><strong>About</strong></p> <p>This data represents the supplementary material for the conference paper with above title submitted at ISMAR 2021.</p> <p> </p> <p><strong>Contents</strong></p> <p>The supplementary material contains five files:</p> <ol> <li>The abstraction of paraphrases and transcripts after each condition respectively.<br> According to qualitative content analysis procedure, the conducted interviews were transcribed, paraphrased and subsequently abstracted to generate a category system. Every category is described with a definition and some exemplary quotes. Statements of participants are condensed and abstracted. Number of participants who made statements regarding a category, and most prominent valence are taken as basis to generate tree maps in Figure 5 and 6.<br> Please note that the prevalences represent the views or opinions of the participants on the single categories. Also, the mentioned categories have several subcategories and only the most important regarding privacy awareness are mentioned in the article.<br> <br> Transcripts, audio files and paraphrases are available upon request.</li> <li>The experimental task description. It served as exposition for the task that the participants had to complete.</li> <li>The interview guideline. Please note that this study was part of a larger project that also focused on topics such as usability and immersion, however, the article reports only on privacy awareness.</li> <li>The R script file to generate the tree maps in Figures 5 and 6. The dataset is created using data from the abstraction Excel sheet.</li> <li>A demonstration video of the experimental setup.</li> </ol>
Smart home adoption factors: A systematic literature review and research agenda
<p>Smart homes represent the complement of various automation technologies that together make up a network of devices facilitating the daily tasks of residents. These technologies are being studied for their application from different sectors, including the projection of their use to improve energy consumption planning and health care management. However, technology adoption depends on social awareness within the scope of cognitive advantages and innovations compared to perceived risk because although there are multiple benefits, potential users express fears related to the loss of autonomy and security. This study carries out a systematic literature review based on PRISMA in order to analyze research trends and literary evolution in the technological adoption of smart homes, considering the main theories and variables applied by the community. In proposing a research agenda in accordance with the identified gaps and the growing and emerging themes of the object of study, it is worth highlighting the growing interest in the subject, both for the present and its development in the future. Until now, adoption factors have been attributed more to the technological acceptance model and the diffusion of innovation theory, adopting components of the Theory of Planned Behavior; therefore, in several cases, the attributes of different theories are merged to adapt to the needs of each researcher, promoting the creation of empirical and extended models.</p>
User Involvement in Smart Home Learning
<p>KI-basierte Smart Homes sind in der Regel darauf ausgelegt, vollständig autonom zu arbeiten. Aufgrund dessen berichten viele Nutzer:innen doch jedoch von einem Gefühl des Kontrollverlusts in Verbindung mit mangelndem Verständnis für die Funktionsweise des Systems.</p> <p>In einer ersten Onlinestudie untersuchen wir die Möglichkeit einer besseren User Experience durch Beteiligung der User in der Lernphase eines Smart Homes.</p> <p>In diesem animierten Poster wird kurz in die Thematik und die Studie eingeführt.</p> <p>Detaillierte Informationen befinden sich unter:</p> <p>https://osf.io/hg56x</p>
Automatic Generation of Explanations in Autonomous Systems: Enhancing Human Interaction in Smart Home Environments
<p>The file named “Dataset” is the generation of scenarios and explanations used for the proposal.<br>The file named “Questionnaire answers & data analysis” corresponds to the application of a questionnaire addressed to 118 people.</p>
LIGHT dataset sample for solar smart home pilot - 201801
<p>LIGHT dataset sample is provided for a month (2018/01). It consists of Generation (energy/kWh - 15min intervals) for photovoltaic array and battery storage, household power consumption (power/kW - 1min intervals) and photovoltaic array power (power/kW - 1min intervals).</p>
In-Home Assessment of a Smart Foot Mat for Prevention of Diabetic Foot Ulcers
ClinicalTrials.gov study NCT02647346. IPD Sharing: NO. Countries: 1. Publications: 2.
Smart Lighting for Nursing Home Residents With Dementia
ClinicalTrials.gov study NCT05825404. IPD Sharing: NO. Countries: 1. Publications: 3.
Dataset for ISSTA'22 Understanding Device Integration Bugs in Smart Home System
<p>This is the dataset for the ISSTA`22 submission "Understanding Device Integration Bugs in Smart Home System". It contains 330 device integration bugs collected from the most popular open source SmartHome system, i.e., Home Assistant.</p>
Usability of SMART ANGEL Medical Device to Record and Transmit Health Data From Patient's Home Following Outpatient Surgery
ClinicalTrials.gov study NCT03464721. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Effectiveness of a Smart Community- and Home-based Integrated Care Services System for Elderly People
ClinicalTrials.gov study NCT06848036. IPD Sharing: NO. Countries: 1. Publications: 4.
Smart Home Technologies for Assessing and Monitoring Frailty in Older Adults
ClinicalTrials.gov study NCT05961319. IPD Sharing: NO. Countries: 1. Publications: 4.
Home Rehabilitation Using Smart Wearable Exercise and Electrical Stimulation Device After Anterior Cruciate Ligament Reconstruction
ClinicalTrials.gov study NCT04079205. IPD Sharing: UNDECIDED. Countries: 1. Publications: 1.
Home-based Walking Program With Smart Devices
ClinicalTrials.gov study NCT04113057. IPD Sharing: NO. Countries: 1. Publications: 17.
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