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Dataset results
47 results for “Smart Home”
PREhabilitation of Frail Elderly PAtients Undergoing majoR surgEry at HOME (PREPARE-HOME) Using Smart Wearables
ClinicalTrials.gov study NCT06633614. IPD Sharing: NO. Countries: 0. Publications: 1.
The Effect of Using Smart Phone Application for Enhancing Adherence to Home Exercise
ClinicalTrials.gov study NCT04159883. IPD Sharing: NO. Countries: 1. Publications: 1.
Smart Home-based Technology to Promote Functional Mobility Among Individuals With Parkinson's Disease
ClinicalTrials.gov study NCT05211687. IPD Sharing: NO. Countries: 1. Publications: 2.
A Smart Home-based Exoskeleton Robot System for Stroke Patients
ClinicalTrials.gov study NCT04463888. IPD Sharing: Not stated. Countries: 1. Publications: 1.
CareToy - A Modular Smart System for Infants' Rehabilitation at Home Based on Mechatronic Toys
ClinicalTrials.gov study NCT01990183. IPD Sharing: Not stated. Countries: 2. Publications: 4.
Adoption of Smart Home Services Among Young People
Open the record for dataset details and reuse information.
Synthetic Smart Home Dataset
<p>This dataset is derived from the van Kasteren dataset (van Kasteren, T., Noulas, A., Englebienne, G., Kröse, B.: Accurate activity recognition in a home setting. In: Proceedings of the 10th International Conference on Ubiquitous Computing (UbiComp08), 2008) using a Markov model. The dataset has been validated with a conformance test, yielding positive results.</p> <p>The dataset comprises three columns:</p> <ol> <li><strong>Day</strong>: Represents the day number, with each row corresponding to a specific day.</li> <li><strong>Activities</strong>: Represents the sequence of activities performed on a given day. The activities are listed in the order they occurred. Each activity is denoted by a number corresponding to a specific action: <ul> <li>1: 'leave house'</li> <li>4: 'use toilet'</li> <li>5: 'take shower'</li> <li>10: 'go to bed'</li> <li>13: 'prepare breakfast'</li> <li>15: 'prepare dinner'</li> <li>17: 'get drink'</li> </ul> </li> <li><strong>Durations</strong>: Represents the duration (in minutes) for each corresponding activity in the "Activities" column.</li> </ol> <p>For example, on Day 1, the sequence of activities is recorded as <code>[4, 4, 10, 4, 1, 4, 4, 10, 4]</code>, with corresponding durations <code>[1, 1, 23.8, 1, 202.03, 1.81, 1, 25.86, 1.29]</code>. This indicates that on Day 1, the individual started by spending 1 minute using the toilet, followed by another 1 minute in the toilet, then 23.8 minutes in bed, and so on.</p>
CADeSH Dataset: Collaborative Anomaly Detection for Smart Homes
<p>Dataset used for quantitative evaluation in the paper:</p> <p>Y. Meidan, D. Avraham, H. Libhaber and A. Shabtai, "CADeSH: Collaborative Anomaly Detection for Smart Homes," in IEEE Internet of Things Journal, 2022, doi: 10.1109/JIOT.2022.3194813.</p> <p> </p> <p>This is a table of flow-level traffic data which was continuously captured during a period of 21 days from five real home networks which were subscribed to a smart home security service, and from our lab at Ben-Gurion University of The Negev. This security service provider shared with us these network traffic flows, plus the related DNS requests and responses, and reputation intelligence of the destination IP addresses. Each instance in this dataset represents an outbound network traffic flow (in the form of an IPFIX) which emanated from an instance of the IoT model streamer.Amazon.Fire_TV_Gen_3.</p> <p>In our lab, we infected our streamer.Amazon.Fire_TV_Gen_3 with a cryptominer and executed cryptomining from this device. To imitate a scanning activity typically performed by some botnets, we also scanned the network using Nmap. In accordance, we labeled these malicious activities as (1) `is executing cryptomining,' or (2) `being scanned by Nmap.' All of the remaining IPFIXs captured in our lab or on the home networks were labeled as `assumed benign'.</p> <p>The multitude of real home networks, and the multitude of identical source devices, enable using this dataset for quantitative evaluation of (collaborative) anomaly/attack detection methods, especially for the IoT.</p>
The Application of Smart Health Management System on Home-Care for Elderly Patients
ClinicalTrials.gov study NCT02616575. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Smart Reminders to Promote Home-based Cognitive Training
ClinicalTrials.gov study NCT05016856. IPD Sharing: YES. Countries: 1. Publications: 0.
Clinical Study on Improving Exercise Capacity in Chronic Obstructive Pulmonary Disease Through Smart IoT-Remote Home Breathing Guidance
ClinicalTrials.gov study NCT06963333. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Smart Living Homes: GATEKEEPER System With Patients With Cancer and Dementia in Cyprus
ClinicalTrials.gov study NCT05490524. IPD Sharing: UNDECIDED. Countries: 1. Publications: 0.
SMART@Home Feasibility Trial
ClinicalTrials.gov study NCT06159127. IPD Sharing: Not stated. Countries: 1. Publications: 0.
ICU-Recover Box 2.0, Smart Technology for Home Monitoring of ICU Patients
ClinicalTrials.gov study NCT07162948. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Upgraded Smart Cloth Home Care System for Persons With Dementia
ClinicalTrials.gov study NCT05476809. IPD Sharing: NO. Countries: 1. Publications: 0.
Home-based Exercise Program With Smart Rehabilitation System
ClinicalTrials.gov study NCT03282734. IPD Sharing: NO. Countries: 1. Publications: 0.
Evaluation of the Effectiveness, Safety, and Cost of a Smart Home-based Hospital System for Patients With Interstitial Lung Disease: Prospective Multicenter Randomized Controlled Trial
ClinicalTrials.gov study NCT06601790. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Smart Sleep In-home Validation Extension Study
ClinicalTrials.gov study NCT03665844. IPD Sharing: NO. Countries: 1. Publications: 0.
Evaluation of the Impact of the SMART ANGEL ™ Device on Follow-up at Home Following Major or Intermediate Outpatient Surgery
ClinicalTrials.gov study NCT04068584. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Intelligent Temporal Data Driven World Actuation in Ambient Environments Case Study: Anomaly Recognition and Assistance Provision in Smart Home
Abstract — A possible resident of smart home is an old person or an Alzheimer patient that should be assisted continuously for the rest of his life; however, normally this person desires to live independently at home. Typically, this person may forget sometimes completion of the activities; may realize the activities of daily living incorrectly, and may enter to dangerous states. In this context smart home project is proposed as an ambient intelligent environment, in which on one hand the resident is observed continuously through the embedded sensors, and on the other hand the resident is assisted automatically through the embedded electronically controllable actuators. In this work, we propose an approach to interpret the sensors’ observations and how to automatically reason in the required assistance. The result is provision of automated assistance for the smart home resident.
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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.