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Dataset results
190 results for “wearable device”
Supplementary Materials for "What Your Wearable Devices Revealed About You and Possibilities of Non-Cooperative 802.11 Presence Detection During Your Last IPIN Visit"
<p>Supplementary Materials for "What Your Wearable Devices Revealed About You and Possibilities of Non-Cooperative 802.11 Presence Detection During Your Last IPIN Visit"</p> <p>This package contains an anonymized packet of 802.11 probe requests captured in Lloret de Mar during the Indoor Positioning and Indoor Navigation 2021 conference. The packet capture file is in the standardized *.pcap binary format and can be opened with any packet analysis tool such as Wireshark or scapy (Python packet analysis and manipulation package).</p>
Dataset: Wearable Devices Ltd. (WLDSW) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Dataset: Wearable Devices Ltd. (WLDS) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Dataset for Real-Time Indoor Localization System Based on Wearable Device, Bluetooth Low Energy (BLE) Beacons, and Machine Learning
<p>The dataset titled <strong>"Real-Time Indoor Localization System Based on Wearable Device, Bluetooth Low Energy (BLE) Beacons, and Machine Learning</strong><strong>"</strong> was collected to support the development of an indoor localization system that operates at the room level. The dataset includes measurements of Received Signal Strength Indication (RSSI) from Bluetooth Low Energy (BLE) beacons (specifically the iBKS105 model) recorded by an ESP32 device. These RSSI values were captured across various rooms, allowing for precise localization within an indoor environment. The dataset is particularly useful for research in indoor localization system including machine learning-based localization algorithms.</p>
Wearable Device Dataset from Induced Stress and Structured Exercise Sessions
<p>This original dataset contains physiological signals collected during structured acute stress induction and aerobic and anaerobic exercise sessions using a wearable device. Blood volume pulse, motion-based activity, skin temperature, and electrodermal activity were recorded with the Empatica E4, a research-grade wearable. The stress induction protocol involved math and emotional tasks designed to provoke stress responses, interleaved with rest periods. Self-reported stress levels were also recorded during this procedure. For the exercise sessions, distinct routines on a stationary bike were created for aerobic and anaerobic activities. The dataset includes records from 36 healthy volunteers for stress sessions, 30 for aerobic exercise, and 31 for anaerobic exercise. By examining the variations in physiological signals, the effects of these activities can be analyzed. This dataset is a valuable resource for research on stress and exercise detection and classification.</p>
A non-contact wearable device for monitoring epidermal molecular flux
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Raw Data for Energy Consumption Comparison of DMA-Based and FatFs Storage Systems on Wearable Devices
<p>This dataset contains raw oscilloscope measurements comparing the energy consumption of a Direct Memory Access (DMA)-based storage system versus the FatFs file system for wearable devices. The data was collected as part of the study "Direct Memory Access-Based Data Storage for Long-Term Acquisition Using Wearables in an Energy-Efficient Manner".</p> <p>The dataset includes voltage drop measurements across a 2-ohm shunt resistor, captured using an Analog Discovery 2 digital oscilloscope at a 500 kHz sampling rate. Measurements were taken under various conditions:</p> <ul> <li>Storage systems: DMA-based (proposed) and FatFs</li> <li>SD card capacities: 4 GB and 8 GB</li> <li>Write frequencies: 2 Hz and 5 Hz (referring to the frequency of writing a specific data block of 15,872 bytes)</li> <li>With and without a smoothing capacitor</li> </ul> <p>Each CSV file contains 20 million samples, equivalent to 40 seconds of data acquisition. File names encode the experimental conditions, including the storage system, write frequency, number of samples, acquisition rate, acquisition time, data format, SD card size, and absence of the smoothing capacitor.</p> <p>The data is organized into two main folders:</p> <ol> <li>"cap": Contains measurements with the smoothing capacitor</li> <li>"no_cap": Contains measurements without the smoothing capacitor</li> </ol> <p>This raw data can be used to reproduce the energy consumption and write speed analyses presented in the article, as well as for further investigation into the performance of embedded storage systems for wearable devices.</p>
Evaluation of tracking devices and elicitation of wearability requirements for animal-centred biotelemetry in cats
<p>Thirteen cat participants wearing GPS devices were monitored through ethologically-informed observations, designed specifically to measure the behaviour of the animals with the biotelemetry tags. Here, findings from the behavioural analysis are presented.</p>
Wearable Device Intervention to Improve Sun Behaviors in Melanoma Survivors
ClinicalTrials.gov study NCT03927742. IPD Sharing: NO. Countries: 1. Publications: 2.
Wearable Pulsed Electromagnetic Fields Device in Knee Osteoarthritis: Double Blinded, Randomized Clinical Trial
ClinicalTrials.gov study NCT01877278. IPD Sharing: Not stated. Countries: 1. Publications: 4.
Wearable Assisted Viral Evidence (WAVE) Study A Decentralized, Prospective Study Exploring the Relationship Between Passively-collected Data From Wearable Activity Devices and Respiratory Viral Infect
ClinicalTrials.gov study NCT06207929. IPD Sharing: UNDECIDED. Countries: 1. Publications: 9.
Efficacy of the Quell Wearable Device for Fibromyalgia
ClinicalTrials.gov study NCT03714425. IPD Sharing: NO. Countries: 1. Publications: 1.
Wearable Device and Self-Regulation Strategies to Promote Physical Activity Among Children With Cancer: A Pilot Study
ClinicalTrials.gov study NCT07003607. IPD Sharing: NO. Countries: 1. Publications: 2.
Eye-Control Trial: Wearable Eye-Tracking Device as Means of Communication
ClinicalTrials.gov study NCT04582149. IPD Sharing: YES. Countries: 1. Publications: 1.
Wearable LITUS Device for Osteoarthritis of the Knee: a Randomized Double-Blind Placebo-Controlled Trial
ClinicalTrials.gov study NCT02083861. IPD Sharing: Not stated. Countries: 1. Publications: 1.
A Novel Wearable Device to Improve Sleep Quality
ClinicalTrials.gov study NCT05952297. IPD Sharing: Not stated. Countries: 1. Publications: 1.
A Ring-type Wearable Device for Atrial Fibrillation
ClinicalTrials.gov study NCT04023188. IPD Sharing: UNDECIDED. Countries: 1. Publications: 1.
Data from: physiological and emotional assessment of college students using wearable and mobile devices during the 2020 COVID-19 lockdown: an intensive, longitudinal dataset
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WEEE, A Multi-Device and Multi-Modal Dataset for Wearable Human Energy Expenditure Estimation
<p>We present WEEE, a multi-device and multi-modal dataset collected from 17 participants under different physical activities.<br> WEEE contains: 1) sensor data collected using 7 wearable devices placed on 4 body locations - head, ear, chest, and wrist<br> -, 2) respiratory data collected with an indirect calorimeter serving as ground-truth information, 3) demographics and body<br> composition data (e.g., muscle or fat percentage), 4) activity type - and their corresponding metabolic equivalent of task (MET) values - and intensity level, and 5) answers to questionnaires related to physical activity level, diet, stress and sleep. Thanks to the diversity of sensors and body locations of the WEEE dataset, we envision that this dataset will enable the development of novel human energy expenditure estimation techniques for a diverse set of application scenarios. Energy expenditure (EE) refers to the amount of energy an individual uses to maintain body functions and as a result of physical activity. The ability to estimate EE allows computing systems obtaining valuable insights regarding people’s physical activity and providing personalized recommendations for promoting a healthier and more active lifestyle.</p>
Data from: Characterizing terminology applied by authors and database producers to informatics literature on consumer engagement with wearable devices
<p><span>To recommend strategies to improve discoverability of consumer health informatics (CHI) literature, we aimed to characterize controlled vocabulary and author terminology applied to a subset of CHI literature on wearable technologies. </span>A descriptive analysis of articles (N=2,522) from 2019 identified 308 (12.2%) CHI-related articles for which the citations with PubMed identifiers for the included and excluded studies are provided. The 308 articles were published in 181 journals which we classified by type of journal—health, informatics, technology and other—as shown in the third file. We provide an aggregated file of the author-assigned keywords as they appeared in the PubMed records of the included studies along with our decision about whether they represented consumer engagement. We also included an aggregated file of the Medical Subject Headings assigned to the included studies. The top 100 terms and their frequency scores for the title and abstracts are also included. We did not include any of the terminology from CINAHL, and Engineering Databases (Compendex and Inspec together) due to copyright concerns.</p>
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
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.