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Dataset results
155 results for “Accelerometer”
Micra Atrial Tracking Using a Ventricular Accelerometer Study
ClinicalTrials.gov study NCT03157297. IPD Sharing: NO. Countries: 9. Publications: 1.
A Biosensor for Tracking Seizures: Linking a Wrist Accelerometer to an Online Epilepsy Diary
ClinicalTrials.gov study NCT02177877. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Data from: A comparison of techniques for classifying behaviour from accelerometers for two species of seabird
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Data from: Analysis of animal accelerometer data using hidden Markov models
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Systematic review of validation of supervised machine learning models in accelerometer-based animal behaviour classification literature
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Data from: Accelerometers can measure total and activity-specific energy expenditure in free-ranging marine mammals only if linked to time-activity budgets
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Pacific black ducks tri-axial accelerometer data with behaviour labels
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Accelerometer-based network analysis in female soccer: performance levels
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Westland petrel data combined GPS and accelerometer data 2016 & 2017
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Insights into short and long-term crop-foraging strategies in a chacma baboon (Papio ursinus) from GPS and accelerometer data
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Smartphone Gyroscope and Accelerometer Dataset for Human Activity Recognition
<p>This dataset is from a study in which we collected smartphone accelerometer and gyroscope data of four subjects while they performed various activities. Two iPhone smartphones were used, with one in the front pants pocket and the other in the back pants pocket. The datasets have been annotated with ground truth activity labels based on video footage. The study procedure is described in our paper: </p> <p>Huang, E.J. and Onnela, J.P. Augmented Movelet Method for Activity Classification Using Smartphone Gyroscope and Accelerometer Data. Sensors 2020, 20, 3706.</p>
Data from: Performance improvement of miniaturized ZnO nanowire accelerometer fabricated by refresh hydrothermal synthesis
Miniaturized accelerometers are necessary for evaluating the performance of small devices such as haptics, robotics and simulators. In this study, we fabricated miniaturized accelerometers using well aligned ZnO nanowires. The layer of ZnO nanowires is used for active piezoelectric layer of the accelerometer and copper was chosen as a head mass. Seedless and refresh hydrothermal synthesis methods were conducted to grow ZnO nanowires on the copper substrate and effect of ZnO nanowire length on the accelerometer performance was investigated. The refresh hydrothermal synthesis exhibits longer ZnO nanowires, 12 μm, than the seedless hydrothermal synthesis, 6 μm. Performance of the fabricated accelerometers was verified by comparing with a commercial accelerometer. The sensitivity of the fabricated accelerometer by the refresh hydrothermal synthesis is shown to be 37.7 pA/g, which is about 30 times larger than the previous result.
Dataset for "Exceptional-point-based accelerometers with enhanced signal-to-noise ratio"
<p>Data set supporting the data represented in the article </p>
White stork tri-axial accelerometer data with behaviour labels
<p>The tri-axial ACC demo dataset from white stork (<i>Ciconia ciconia</i>) (data accessible from the AcceleRater website: http://accapp.move-ecol-minerva.huji.ac.il/) was measured at 10.54 Hz. Forty tri-axial measurements, totalling 3.8 seconds, were used to form a behaviour segment. The dataset includes 1746 segments each forming a row in the dataset. Each row contains 121 columns. The first 120 columns are ACC measurements from three orthogonal axes, arranged as x, y, z, x, y, z, ...,x, y, z. The final column is of type character containing the corresponding behaviour. The dataset contains 5 different behaviours including "A_FLIGHT" - active flight (77 cases), "P_FLIGHT" - passive filght (96), "WALK" - walking (437), "STND" - standing (863), "SITTING" - sitting (273).</p>
Time variation of accelerometer sensor data from a smartphone placed on the same surface next to the mobilefuge
<p>Time variation of accelerometer sensor data from a smartphone placed on the same surface next to the mobilefuge is recorded to show the vibrations caused by the mobilefuge.</p> <p>This data set has three csv files that are used to create the Figure 9 in the mobilefuge article. </p> <p>Figure.9a_mobilefuge_off.csv</p> <p>Fugure.9b_mobilefuge_on.csv</p> <p>Figure.9c_mobilefuge_with_pad.csv</p> <p>The labels in the first row of each of the file are Time (s), acceleration along the x-direction (m/s^2), acceleration along the y-direction (m/s^2) and acceleration along the z-direction (m/s^2).</p>
Data from: Using hidden Markov models to improve quantifying physical activity in accelerometer data – a simulation study
Introduction The use of accelerometers to objectively measure physical activity (PA) has become the most preferred method of choice in recent years. Traditionally, cutpoints are used to assign impulse counts recorded by the devices to sedentary and activity ranges. Here, hidden Markov models (HMM) are used to improve the cutpoint method to achieve a more accurate identification of the sequence of modes of PA. Methods:1,000 days of labeled accelerometer data have been simulated. For the simulated data the actual sedentary behavior and activity range of each count is known. The cutpoint method is compared with HMMs based on the Poisson distribution (HMM[Pois]), the generalized Poisson distribution (HMM[GenPois]) and the Gaussian distribution (HMM[Gauss]) with regard to misclassification rate (MCR), bout detection, detection of the number of activities performed during the day and runtime. Results:The cutpoint method had a misclassification rate (MCR) of 11% followed by HMM[Pois] with 8%, HMM[GenPois] with 3% and HMM[Gauss] having the best MCR with less than 2%. HMM[Gauss] detected the correct number of bouts in 12.8% of the days, HMM[GenPois] in 16.1%, HMM[Pois] and the cutpoint method in none. HMM[GenPois] identified the correct number of activities in 61.3% of the days, whereas HMM[Gauss] only in 26.8%. HMM[Pois] did not identify the correct number at all and seemed to overestimate the number of activities. Runtime varied between 0.01 seconds (cutpoint), 2.0 minutes (HMM[Gauss]) and 14.2 minutes (HMM[GenPois]). Conclusions: Using simulated data, HMM-based methods were superior in activity classification when compared to the traditional cutpoint method and seem to be appropriate to model accelerometer data. Of the HMM-based methods, HMM[Gauss] seemed to be the most appropriate choice to assess real-life accelerometer data.
Actigraph Accelerometer Validation Study
ClinicalTrials.gov study NCT00342212. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Accelerometer Based Objective Clinical Outcomes of Spinal Cord Stimulation (SCS) and Peripheral Nerve Stimulation (PNS)
ClinicalTrials.gov study NCT02948049. IPD Sharing: NO. Countries: 0. Publications: 11.
Functionality of the Pulse Oximeter and Accelerometer Portions of a Novel Wearable Device
ClinicalTrials.gov study NCT05197790. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Validity of an Actigraph Accelerometer Following Critical Illness
ClinicalTrials.gov study NCT03295630. IPD Sharing: NO. Countries: 1. Publications: 0.
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
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DANDI Archive for NWB datasets
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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.