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152 results for “Biometric”
Novel Self-charging, Medical-Grade Smart Insoles With AI/ML Edge Computing to Monitor Biometrics.
ClinicalTrials.gov study NCT07273422. IPD Sharing: NO. Countries: 1. Publications: 0.
mHealth Biometrics for Sedentary People (MotivateLJMU)
ClinicalTrials.gov study NCT04979702. IPD Sharing: NO. Countries: 1. Publications: 0.
Integrating Electronic Patient Reported Biometric Measures (ePReBMs) From Wearable Devices in Respiratory Diseases
ClinicalTrials.gov study NCT04857814. IPD Sharing: Not stated. Countries: 1. Publications: 0.
BIOmetric MEasurements in Diagnostics: Comparison of EXperts and IA-assisted Residents
ClinicalTrials.gov study NCT06892327. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Mobile Cardiac Telemetry and Advanced Multi-Parameter Monitoring in PatientS Wearing a Novel Device With Biometric Based Medications Changes
ClinicalTrials.gov study NCT05505136. IPD Sharing: NO. Countries: 1. Publications: 0.
Identification of Clinical, Biometrical and Operatory Factors Related to Pain During Cataract Surgery
ClinicalTrials.gov study NCT04353843. IPD Sharing: UNDECIDED. Countries: 1. Publications: 0.
Data from: Comparison of biometrical models for joint linkage association mapping
Open the record for dataset details and reuse information.
Impact of breed and sex on porcine endocrine transcriptome: A Bayesian biometrical analysis
GEO Series GSE14739. Sus scrofa. 80 samples. Type: Expression profiling by array.
FIGURE 4 in Comparative morphological and biometrical studies on Trhypochthonius species of the tectorum species group (Acari: Oribatida: Trhypochthoniidae)
FIGURE 4. Profiles of all notogastral setae. a—Trhypochthonius tectorum; b—T. americanus; c—T. silvestris; d—T. silvestris europaeus subsp. nov.; e—T. japonicus forma occidentalis.
FIGURE 14 in Comparative morphological and biometrical studies on Trhypochthonius species of the tectorum species group (Acari: Oribatida: Trhypochthoniidae)
FIGURE 14. Ranges of length-values of notogaster setae (a, c) and individual length-values plotted against notogaster lengths (b, d) of Trhypochthonius species. a, b—d1 setae; c, d—e1 setae. Abbreviations of species at plots: see fig. 12.
BED: Biometric EEG dataset
<p><strong>The BED dataset</strong></p> <p> </p> <p><strong>Version 1.0.0</strong></p> <p> </p> <p><strong>Please cite as:</strong> Arnau-González, P., Katsigiannis, S., Arevalillo-Herráez, M., Ramzan, N., "BED: A new dataset for EEG-based biometrics", IEEE Internet of Things Journal, vol. 8, no. 15, pp. 12219 - 12230, 2021.</p> <p> </p> <p><strong>Disclaimer</strong></p> <p>While every care has been taken to ensure the accuracy of the data included in the BED dataset, the authors and the University of the West of Scotland, Durham University, and Universitat de València do not provide any guaranties and disclaim all responsibility and all liability (including without limitation, liability in negligence) for all expenses, losses, damages (including indirect or consequential damage) and costs which you might incur as a result of the provided data being inaccurate or incomplete in any way and for any reason. 2020, University of the West of Scotland, Scotland, United Kingdom.</p> <p> </p> <p><strong>Contact</strong></p> <p>For inquiries regarding the BED dataset, please contact:</p> <ol> <li>Dr Pablo Arnau-González, arnau.pablo [*AT*] gmail.com</li> <li>Dr Stamos Katsigiannis, stamos.katsigiannis [*AT*] durham.ac.uk</li> <li>Prof. Miguel Arevalillo-Herráez, miguel.arevalillo [*AT*] uv.es</li> <li>Prof. Naeem Ramzan, Naeem.Ramzan [*AT*] uws.ac.uk</li> </ol> <p> </p> <p><strong>Dataset summary</strong></p> <p>BED (Biometric EEG Dataset) is a dataset specifically designed to test EEG-based biometric approaches that use relatively inexpensive consumer-grade devices, more specifically the Emotiv EPOC+ in this case. This dataset includes EEG responses from 21 subjects to 12 different stimuli, across 3 different chronologically disjointed sessions. We have also considered stimuli aimed to elicit different affective states, so as to facilitate future research on the influence of emotions on EEG-based biometric tasks. In addition, we provide a baseline performance analysis to outline the potential of consumer-grade EEG devices for subject identification and verification. It must be noted that, in this work, EEG data were acquired in a controlled environment in order to reduce the variability in the acquired data stemming from external conditions.</p> <p>The stimuli include:</p> <ul> <li>Images selected to elicit specific emotions</li> <li>Mathematical computations (2-digit additions)</li> <li>Resting-state with eyes closed</li> <li>Resting-state with eyes open</li> <li>Visual Evoked Potentials at 2, 5, 7, 10 Hz - Standard checker-board pattern with pattern reversal</li> <li>Visual Evoked Potentials at 2, 5, 7, 10 Hz - Flashing with a plain colour, set as black</li> </ul> <p>For more details regarding the experimental protocol and the design of the dataset, please refer to the associated publication: Arnau-González, P., Katsigiannis, S., Arevalillo-Herráez, M., Ramzan, N., "BED: A new dataset for EEG-based biometrics", IEEE Internet of Things Journal, 2021. (Under review)</p> <p> </p> <p><strong>Dataset structure and contents</strong></p> <p>The BED dataset contains EEG recordings from 21 subjects, acquired during 3 similar sessions for each subject. The sessions were spaced one week apart from each other.</p> <p>The BED dataset includes:</p> <ul> <li>The raw EEG recordings with no pre-processing and the log files of the experimental procedure, in text format</li> <li>The EEG recordings with no pre-processing, segmented, structured and annotated according to the presented stimuli, in Matlab format</li> <li>The features extracted from each EEG segment, as described in the associated publication</li> </ul> <p>The dataset is organised in 3 folders:</p> <ul> <li>RAW</li> <li>RAW_PARSED</li> <li>Features</li> </ul> <p> </p> <p><strong>RAW/</strong> Contains the RAW files<br> <strong>RAW/sN/</strong> Contains the RAW files associated with subject <em>N</em><br> Each folder <strong>sN</strong> is composed by the following files:<br> <strong>- sN_s1.csv, sN_s2.csv, sN_s3.cs</strong>v -- Files containing the EEG recordings for subject <em>N</em> and session 1, 2, and 3, respectively. These files contain 39 columns:<br> COUNTER INTERPOLATED F3 FC5 AF3 F7 T7 P7 O1 O2 P8 T8 F8 AF4 FC6 F4 ...UNUSED DATA... UNIX_TIMESTAMP<br> <strong>- subject_N_session_1_time_X.log, subject_N_session_2_time_X.log, subject_N_session_3_time_X.log</strong> -- Log files containing the sequence of events for the subject N and the session 1,2, and 3 respectively.</p> <p> </p> <p><strong>RAW_PARSED/</strong><br> Contains Matlab files named <strong>sN_sM.mat</strong>. The files contain the recordings for the subject <em>N</em> in the session <em>M</em>. These files are composed by two variables:<br> <strong>- <em>recording</em></strong>: size (time@256Hz x 17), Columns: COUNTER INTERPOLATED F3 FC5 AF3 F7 T7 P7 O1 O2 P8 T8 F8 AF4 FC6 F4 UNIX_TIMESTAMP<br> <strong>-<em> events</em></strong>: cell array with size (events x 3) START_UNIX END_UNIX ADDITIONAL_INFO<br> START_UNIX is the UNIX timestamp in which the event starts<br> END_UNIX is the UNIX timestamp in which the event ends<br> ADDITIONAL INFO contains a struct with additional information regarding the specific event, in the case of the images, the expected score, the voted score, in the case of the cognitive task the input, in the case of the VEP the pattern and the frequency, etc..</p> <p> </p> <p><strong>Features/</strong><br> <strong>Features/Identification</strong><br> <strong>Features/Identification/[ARRC|MFCC|SPEC]/</strong>: Each of these folders contain the extracted features ready for classification for each of the stimuli, each file is composed by two variables, "feat" the feature matrix and "Y" the label matrix.<br> <strong><em>- feat</em></strong>: N x number of features<br> <strong><em>- Y</em></strong>: N x 2 (the #subject and the #session)<br> <strong><em>- INFO</em></strong>: Contains details about the event same as the ADDITIONAL INFO<br> <strong>Features/Verification</strong>: This folder is composed by 3 different files each of them with one different set of features extracted. Each file is composed by one cstruct array composed by:<br> <strong><em>- data</em></strong>: the time-series features, as described in the paper<br> <strong><em>- y</em></strong>: the #subject<br> <strong><em>- stimuli</em></strong>: the stimuli by name<br> <em><strong>- session</strong></em>: the #session<br> <strong><em>- INFO</em></strong>: Contains details about the event</p> <p>The features provided are in sequential order, so index 1 and index 2, etc. are sequential in time if they belong to the same stimulus.</p> <p> </p> <p><strong>Additional information</strong></p> <p>For additional information regarding the creation of the BED dataset, please refer to the associated publication: Arnau-González, P., Katsigiannis, S., Arevalillo-Herráez, M., Ramzan, N., "BED: A new dataset for EEG-based biometrics", IEEE Internet of Things Journal, vol. 8, no. 15, pp. 12219 - 12230, 2021.</p> <p> </p>
Biometric sensor and smartwatch response data collected during VR user studies for Worker Safety III
<p>Two related datasets are included documenting users' sensor data and responses to alarms on a smartwatch during virtrual reality (VR) worker safety experiments on an integrated traffic simulation and VR platform.</p> <ul> <li><strong>watchData.csv </strong>records the times at which the user received an alarm on an Apple Watch and times when they tapped the watch screen to acknowledge the alarm.</li> <li><strong>HRData.csv</strong> records blood volume pulse (BVP), inter-beat interval (IBI), and heart rate (HR) data for each user Data was recorded using an Empatica E4 PPG (photoplethysmography) wristband.</li> </ul> <p>Both csv files shared the same column headers with the following descriptions:</p> <ul> <li>UserID - Each user has a unique 4 digit ID randomly assigned to them at their first VR experiment.</li> <li>Scenario - Indicates which work zone safety Scenario the user experienced. <ul> <li>Scenario 1: Placing cones in a mobile work zone</li> <li>Scenario 2: Sensor installation on highway</li> <li>Scenario 3: Surveying in urban intersections</li> </ul> </li> <li>Trial - Indicates how many times they have experienced the same VR scenario (i.e. Scenario 3, Trial 2 means the user is experiencing Scenario 3 for the second time)</li> <li>VibrationOnly - Indicates the modality of alarms the user received during the trial. <ul> <li>VibrationOnly = 1 indicates that the alarms were haptic/vibrations only.</li> <li>VibrationOnly = 0 indicates that the alarms were haptic/vibrations AND sounds.</li> </ul> </li> <li>Timestamp - Local timestamp of the data recording</li> <li>Time Elapsed - Time that has passed since the start of the trial</li> <li>From - Indicates the source of the data <ul> <li>From = PPG - indicates the data is from the heart rate sensor</li> <li>From = Watch - indicates the data is from the Apple smartwatch, generally applies to when the user has responded on the watch/acknowledged the watch alarm</li> <li>From = VR - indicates the data is from the traffic simulation, generally applies to when speeding/collision cars trigger an alarm on the Apple smartwatch</li> <li>From = Ultrasonic - indicates the data is from an ultrasonic sensor detecting when the user was on the edge of the work zone perimeter.</li> </ul> </li> <li>Event - indicates the event being recorded <ul> <li>BVP/IBI/HR - indicates the meaning of the three numbers in the Values column (see Values below)</li> <li>Start Simulation - indicates when the trial began</li> <li>Received car approaching alert, <strong>mode=x, id=xxx</strong> <ul> <li><strong>Mode </strong>indicates the type of alarm duration, frequency, and number of repetitions.</li> <li>ID indicates the cause of the alarm</li> </ul> </li> </ul> </li> <li>Type - indicates additional categorizations of events <ul> <li>Speeding/Collision - indicates the alarm was caused by a speeding or collision car</li> <li>Perimeter - indicates the alarm was triggered when the user was on the edge of the work zone perimeter.</li> <li>Response - indicates the user responded/acknowledged the alarm on the smartwatch by tapping the watch screen.</li> <li>BVP/IBI/HR - indicates the meaning of the three numbers in the Values column (see Values below)</li> </ul> </li> <li>Values <ul> <li>For Type=Response, Values indicates the alarm id the user responded to. Response time has to be calculated by subtracting the Time Elapsed of the Response row with the corresponding Time Elapsed of the alarm id row</li> <li>For Type="Received...", Values indicates the alarm mode and id</li> <li>For Type=BVP/IBI/HR, three values are listed indicating respectively blood volume pulse (BVP), inter-beat interval (IBI), and heart rate (HR) data</li> </ul> </li> </ul> <p>Data requests and inquiries can be made by contacting Dr. Semiha Ergan at semiha@nyu.edu</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.