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Dataset results
659 results for “wearables”
Actigraphy, Wearable EEG Band and Smartphone for Sleep Staging
ClinicalTrials.gov study NCT04943562. IPD Sharing: NO. Countries: 1. Publications: 1.
Wearable Robotic System and Robotic Mirror Therapy in Spastic Hemiplegia Post Botulinum Toxin Injection
ClinicalTrials.gov study NCT04826900. IPD Sharing: NO. Countries: 1. Publications: 58.
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.
MONITOR-OA: Using Wearable Activity Trackers to Improve Physical Activity in Knee Osteoarthritis
ClinicalTrials.gov study NCT02315664. IPD Sharing: NO. Countries: 1. Publications: 2.
SuPRA: Using Wearable Activity Trackers With a New Application to Improve Physical Activity in Knee Osteoarthritis
ClinicalTrials.gov study NCT02585323. IPD Sharing: NO. Countries: 1. Publications: 1.
Feasibility of a Wearable-enabled Intervention for Promoting Physical Activity in People Knee OA
ClinicalTrials.gov study NCT02313506. IPD Sharing: NO. Countries: 1. Publications: 2.
Multimodal Sleep Intervention Using Wearable Technology
ClinicalTrials.gov study NCT03658954. IPD Sharing: NO. Countries: 1. Publications: 3.
walk2Wellness: Long-term Effects of Walkasins® Wearable Sensory Prosthesis
ClinicalTrials.gov study NCT03538756. IPD Sharing: NO. Countries: 1. Publications: 2.
Towards human-resolution haptics: A high bandwidth, high density, wearable tactile display
Open the record for dataset details and reuse information.
Soft, wearable, microfluidic system for fluorometric analysis of loss of amino acids through eccrine sweat
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CMT1A-BioStampNPoint2023: Charcot-Marie-Tooth disease type 1A accelerometry dataset from three wearable sensor study
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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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Use of wearable sensors to assess compliance of asthmatic children in response to lockdown measures for the COVID-19 epidemic
<p>Dataset from LIFE-MEDEA participants (asthmatic children) from Cyprus and Greece, including Study ID, gender, age, study year, ambient temperature, ambient humidity, recording day, percentage of time staying at home, steps per day, callendar day, calendar week, date, lockdown status (phase 1, 2, or 3) due to COVID-19 pandemic, and if the date was during the weekend (binary variable).</p>
Comparative analysis of positioning accuracy of Garmin Forerunner wearable GNSS receivers in dynamic testing
<p>Required dataset for the manuscript.</p>
Real-World Gait Speed Estimation: Laboratory and Real-Life Patient Data from TOLIFE Wearables
<div><strong>TOLIFE Wearable Dataset Collection</strong></div> <p>This data collection was carried out in the context of the TOLIFE Horizon Europe project, which aims to develop and validate artificial intelligence (AI) solutions for processing patient data collected through non-intrusive wearable sensors. The ultimate goal is to enable personalized treatment, monitor health outcomes, and improve the quality of life of patients with Chronic Obstructive Pulmonary Disease (COPD).</p> <p>The dataset is composed of four distinct subsets, each acquired under different conditions and with specific goals:</p> <div><strong>1. </strong>Gait Speed Dataset</div> <div><strong>2.</strong> Activity Recognition Dataset</div> <div><strong>3. </strong>Validation Dataset</div> <div><strong>4. </strong>Daily Life Dataset</div> <div> </div> <p><strong>1. Gait Speed Dataset:</strong></p> <div><strong>Objective:</strong> To collect reference gait speed data under controlled conditions using wearable sensors, with a focus on validating gait speed estimation algorithms.</div> <div> </div> <div><strong>Materials:</strong></div> <div>Smartphone: Samsung Galaxy A14</div> <div>Smartwatch: Samsung Galaxy Watch 5</div> <div>Smart shoes (custom prototype)</div> <p><strong>Acquisition Protocol:</strong></p> <div>Participants: 25 healthy individuals (10 M, 15 F), age: 28.9 ± 4.8 years</div> <div>Test: Six Minute Walking Test (6MWT) along a 10-meter path</div> <div>Paces: Slow, Medium, Fast (self-selected)</div> <div> </div> <div>Device Placement:</div> <div>- Watch: left </div> <div>- Phone: left front pocket (screen facing thigh, Y-axis upward)</div> <div>- Reference System: Xsens Awinda (17 wireless IMUs) with MVN Analyze software</div> <div>- Reference gait speed = horizontal velocity of Center of Mass (CoM)</div> <p><strong>Folder Content:</strong></p> <div>Wearable folder: individual .csv files for each sensor (phone, watch, shoes)</div> <div>Reference folder: CoM speed from Xsens + walked distances in smwd.csv</div> <div> </div> <p><strong>2. Activity Recognition Dataset:</strong></p> <div><strong>Objective:</strong></div> <div>To build a dataset for training AI models to recognize different daily activities (e.g., resting, walking, stair climbing) using wearable sensor data.</div> <p><strong>Materials:</strong></p> <div>Same TOLIFE wearable platform as above (phone, watch, smart shoes).</div> <p><strong>Acquisition Protocol:</strong></p> <div>Participants: 15 healthy individuals (7 F, 8 M), age: 27.3 ± 3 years</div> <div>Activities:</div> <div>- Resting: lying, sitting, standing (max 5 sec walking allowed)</div> <div>- Walking: 3 sessions (2 x 180 m + 1 x 450 m with directional changes)</div> <div>- Stair climbing: continuous ascent and descent for ~2 minutes</div> <div>Smartwatch placement: user's preferred wrist</div> <p><strong>Folder Content:</strong></p> <div>Data folder: wearable sensor data for each recording session</div> <div>Timestamps folder: .csv files with annotated start/stop times and activity labels</div> <div> </div> <p><strong>3. Validation Dataset:</strong></p> <div><strong>Objective:</strong></div> <div>To test the robustness of trained activity and gait estimation models in less controlled, real-world environments.</div> <p><strong>Materials: </strong>Same TOLIFE wearable platform.</p> <p><strong>Acquisition protocol:<br></strong>Participants: 10 healthy adults (5 M, 5 F), age: 43.8 ± 12.1 years<br>Protocol: free walking outdoors, with mixed activities:<br>- Resting, level walking, stair climbing<br>- 4 walking paths of known length (100–280)<br>- Device placement: user’s preferred side (watch), front pants pocket (phone)</p> <p><strong>Folder Content:<br></strong>Data folder: continuous sensor data recordings<br>Timestamps folder: annotated transitions between activities</p> <p> </p> <div><strong>4. Daily Life Dataset:</strong></div> <div> </div> <div><strong>Objective: </strong>To capture real-world data from COPD patients in daily life conditions for long-term monitoring of mobility and gait speed.</div> <p><strong>Materials: </strong>TOLIFE wearable platform (smartphone, smartwatch, smart shoes)</p> <p><strong>Acquisition Protocol:<br></strong>Participants: 38 COPD patients (23 M, 15 F), age: 63.3 ± 5.8 years<br>Duration: continuous data collection over two weeks<br>Initial Reference Visit (RV):<br>- 2 clinical 6MWTs performed by a pulmonologist, average distance used as a reference value<br>- Instructions: participants used the devices freely (indoors/outdoors) in comfortable conditions</p> <p><strong>Folder Content:<br></strong>Data folder: two weeks of wearable sensor recordings per patient<br>Clinical Six_Minute_Walking_Distance.csv: distances from RV</p> <p> </p> <p>Citation:</p> <p>When using any portion of this dataset collection, please cite:</p> <p>Zanoletti, M.; Bufano, P.; Bossi, F.; Di Rienzo, F.; Marinai, C.; Rho, G.; Vallati, C.; Carbonaro, N.; Greco, A.; Laurino, M.; et al. Combining Different Wearable Devices to Assess Gait Speed in Real-World Settings. Sensors 2024, 24, 3205. https://doi.org/10.3390/s24103205</p>
Improving the reliability of underwater gait analysis using wearable pressure and inertial sensors
<p><strong>Improving the reliability of underwater gait analysis using wearable pressure and inertial sensors</strong></p> <p> </p> <p>This work addresses the lack of reliable wearable methods to assess walking gaits in underwater environments by evaluating the lateral hydrodynamic pressure exerted on lower limbs. Sixteen healthy adults were outfitted with waterproof wearable inertial and pressure sensors. Gait analysis was conducted on land in a motion analysis laboratory using an optoelectronic system as reference, and subsequently underwater in a rehabilitation swimming pool. Differences between the normalized land and underwater gaits were evaluated using temporal gait parameters, knee joint angles and the total water pressure on the lower limbs. The proposed method was validated against the optoelectronic system on land; gait events were identified with low bias (0.01s) using Bland-Altman plots for the stride time, and an acceptable error was observed when estimating the knee angle (10.96° RMSE, Bland-Altman bias -2.94°). The kinematic differences between the land and underwater environments were quantified, where it was observed that the temporal parameters increased by more than a factor of two underwater (p<0.001). The subdivision of swing and stance phases remained consistent between land and water trials. A higher variability of the knee angle was observed in water (CV = 60.75%) as compared to land (CV = 31.02%). The intra-subject variability of the hydrodynamic pressure on the foot (CVz = 39.65%) was found to be substantially lower than that of the knee angle (CVz = 67.69%). The major finding of this work is that the hydrodynamic pressure on the lower limbs may offer a new and more reliable parameter for underwater motion analysis as it provided a reduced intra-subject variability as compared to conventional gait parameters applied in land-based studies.</p> <p> </p>
Wearable SELD Dataset
<p>Wearable SELD dataset is a dataset to develop a sound event localization and detection (SELD) system with wearable devices. The dataset contains recordings collected by using wearable devices such as an earphone, a neck speaker, a headphone, and glasses. Wearable SELD dataset<strong> </strong>has three types of datasets as below.</p> <ul> <li><strong>Earphone type dataset:</strong> It contains recordings collected by 12 microphones placed around the ears mimicking microphones.</li> <li><strong>Mounting type dataset:</strong> It contains recordings collected by 12 microphones placed around the head with some accessories mimicking glasses, a headphone, and a neck speaker.</li> <li><strong>FOA format dataset:</strong> It contains 4 channels recordings collected by an ambisonic microphone to allow comparison with conventional methods using FOA format and those using the above datasets.</li> </ul> <p>Further information is available at https://github.com/nttrd-mdlab/wearable-seld-dataset/</p> <p>License: see the file named LICENSE.pdf</p>
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>
RGB data [front and side views] of the wearable system validation study
<p>This is RGB data of the wearable system validation study. Two video cameras were used. One had the front view. Another was setted at the left side. The recordings were synchronized with Qualisys and Xsens systems.</p> <p>Data structure:</p> <ul> <li>Subjxx: Subject folder <ul> <li>xxxx_front_anonymozied.avi : the front view video of certain movement.</li> <li>xxxx_side_anonymozied.avi : the side view video of certain movement.</li> </ul> </li> </ul> <p> </p> <p> </p> <p> </p> <p> </p>
ScienceDex guides
Understand access before you commit
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.