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Dataset results
155 results for “Accelerometer”
Data from: Performance improvement of miniaturized ZnO nanowire accelerometer fabricated by refresh hydrothermal synthesis
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White stork tri-axial accelerometer data with behaviour labels
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Reliability of heart rate and respiration rate measurements with a wireless accelerometer in postbariatric recovery
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Data from: Using hidden Markov models to improve quantifying physical activity in accelerometer data – a simulation study
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Accelerometer, gyroscope and pressure data associated with behaviors of free-ranging hawksbill sea turtles (Chelonia mydas)
<p> </p> <p>Data accompanying the paper: Jeantet, L., Vigon, V., Geiger, S., & Chevallier, D. (2021). Fully convolutional neural network: A solution to infer animal behaviours from multi-sensor data. <em>Ecological Modelling</em>, <em>450</em>, 109555.. doi : <a title="Persistent link using digital object identifier" href="https://doi.org/10.1016/j.ecolmodel.2021.109555" target="_blank" rel="noreferrer noopener">https://doi.org/10.1016/j.ecolmodel.2021.109555</a></p> <p> </p> <p>In this paper we developped a fully convolutional network, the V-Net, to automatically identify the behaviors of green turtle from acceleration, gyroscope, depth sensor data. With minimal preprocessing, we obtained a F1-score of 81.1% and a Global accuracy of 97.2%. </p> <p> </p> <p><strong>Associated Github with the V-Net script : </strong><a href="https://github.com/jeantetlorene/Vnet_seaturtle_behavior">https://github.com/jeantetlorene/Vnet_seaturtle_behavior</a></p> <p> </p> <p>The dataset comprised the raw acceleration, gyroscope and depth sequence of 13 free-ranging green turtles associated with the behaviors. The indiviuals were equipped with a on-board video recorder combined with an accelerometer, gyroscope, magnetometer and luminosity, temperature and depth sensors using four suction cups and an automatic release system over a two-day periods (see Jeantet et al. 2020 for details and the associated article). The accelerometer, gyroscope, magnetometer recorded at 20 Hz and the pressure, temperature and luminosity sensors at 1 Hz. The cameras were programmed to record until nightfall (6 pm) and resume at daybreak (6 am). The magnetometer, luminosity and temperature data are not provided in this dataset. </p> <p>For each individual, the data collected by the devices was correlated with observed behaviors from video recordings. Unlabeled sequences, primarily night recordings, were excluded, resulting in the creation of one file per day of deployment for each individual. A total of 46 behaviors were observed and are described in detail in Jeantet et al. (2020). The labels for these behaviors are found in the column "beh." The behaviors were grouped into six main categories: Breathing, Feeding, Gliding, Resting, Scratching, and Swimming. Any other observed behavior was categorized as Other. The associated labels for the categories can be found in the column "beh_merge."</p> <p>To process the depth data and increase the sampling rate to 20 Hz, we used a linear interpolation technique. We called this new variable "Pressure_corr". Additionally, we calculated the pressure difference ("Pressure_diff") between each measuring point (originally at 1 Hz).</p> <p>"In total, the green turtle dataset contained 68.6 hours of labelled sequences from 13 individuals (approximately 5.29 hours per individual, max = 14.67 hours, min = 0.96 hours, standard deviation = 3.39 hours). The predominant behavior observed in the videos was Resting, totaling over 34.3 hours, followed by Swimming and Breathing, with 22.3 hours and 5.7 hours, respectively. The other behaviors were expressed in minority (Gliding: 2.3 hours, Feeding: 1.8 hours, Scratching: 1.2 hours and Other: 1 hour). "</p> <p> </p> <p>The folder contains 16 Python matrices, each with 11 columns (AccX, AccY, AccZ, GyrX, GyrY, GyrZ, Depth, beh, beh_merge, Pressure_corr, Pressur_diff) and a number of rows corresponding to the deployment duration. The title of each file indicates the camera number used (CC-07-XX) and the deployment day (DD-MM-YYYY), with an additional number if the file was split due to unlabeled sequences.</p> <p> </p> <p>The folder also contains two dictionaries (behInd_to_behName, behName_to_behInd) that specify the behaviors associated with each number used as a label in the "beh" column. Two dictionaries (behInd_to_behName_cat, behName_to_behInd_cat) that specify the behavioral categories associated with each number used as a label in the "beh_merge" column. Additionally, there is a dictionary (dico_info) that provides the names of the matrix columns and the frequence of recording.</p> <p> </p> <p><strong>Please cite this dataset as :</strong> </p> <p>Jeantet, L., Planas-Bielsa, V., Benhamou, S., Geiger, S., Martin, J., Siegwalt, F., Lelong, P., Gresser, J., Etienne, D., Hielard, G., Arque, A., Regis, S., Lecerf, N., Frouin, C., Benhalilou, A., Murgale, C., Maillet, T., Andreani, L., Campistron, G., … Chevallier, D. (2024). Accelerometer, gyroscope and pressure data associated with behaviors of free-ranging hawksbill sea turtles (Chelonia mydas) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.11643602</p> <p> </p>
Dataset includes raw data collected from accelerometer sensors (6.4 SHM_Sapienza)
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XLTCS - Accelerometer Data Collection in an Epilepsy Monitoring Unit (EMU)
ClinicalTrials.gov study NCT04282681. IPD Sharing: Not stated. Countries: 1. Publications: 0.
The Manikin Study of Chest Compression With One Accelerometer Feedback Device
ClinicalTrials.gov study NCT02073539. IPD Sharing: Not stated. Countries: 1. Publications: 0.
MR Compatible Accelerometer for Respiratory MOTion Measurement
ClinicalTrials.gov study NCT02894632. IPD Sharing: NO. Countries: 1. Publications: 0.
Comparison of Physiologic Response With Rate Adaptive Pacing Driven by Minute Ventilation and Accelerometer
ClinicalTrials.gov study NCT02003378. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Standardized Physical Activities Measured by Accelerometers
ClinicalTrials.gov study NCT01629342. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Accelerometer-linked Online Intervention to Promote Physical Activity in Adolescents
ClinicalTrials.gov study NCT02425384. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Surgical Outcomes of the Accelerometer-based Navigation System for Total Knee Arthroplasty
ClinicalTrials.gov study NCT04037228. IPD Sharing: UNDECIDED. Countries: 1. Publications: 0.
Feasibility of a Consumer Based Accelerometer in Monitoring Outpatient Physical Activity: A Study in Patients With Cancer and Amyotrophic Lateral Sclerosis
ClinicalTrials.gov study NCT02457715. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Study Accelerometer Signals to Measure Daily Activities (SASMDA)
ClinicalTrials.gov study NCT01617512. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Validating Accelerometers to Study Physical Activity of Toddlers
ClinicalTrials.gov study NCT01188044. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Use of Accelerometer for Quantification of Neurogenic Orthostatic Hypotension Symptoms
ClinicalTrials.gov study NCT04782830. IPD Sharing: NO. Countries: 1. Publications: 0.
A Randomised, Double-blind, Placebo-controlled, 12 Week Trial to Evaluate the Effect of Tiotropium Inhalation Capsules (Spiriva) on the Magnitude of Exercise, Measured Using an Accelerometer, in Patie
ClinicalTrials.gov study NCT00144326. IPD Sharing: Not stated. Countries: 3. Publications: 0.
APACE - Feasibility of Using Accelerometers to Measure Physical Activity in Cancer Patients on Early Phase Clinical Trials
ClinicalTrials.gov study NCT06868355. IPD Sharing: Not stated. Countries: 3. Publications: 0.
Combining Accelerometer, Gyroscope, Sound, Electrocardiography and Photoplethysmography Data in Cardiac Monitoring
ClinicalTrials.gov study NCT06422468. IPD Sharing: Not stated. Countries: 1. Publications: 0.
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.