Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
14
datasets available to search
ShareScore release 0.9.0
Dataset results
14 results for “inertial measurement unit”
Multivariate Time Series data of Fatigued and Non-Fatigued Running from Inertial Measurement Units
<p>The data captured came from mounting a single Shimmer3 IMU on the lumbar of 19 recreational runners. The participants were all regular runners and injury free. The study protocol was reviewed and approved by the human research ethics committee at University College Dublin.<br><br>The data was collected in three segments; in the first, the participant completed a 400m run at a comfortable pace; the second segment consisted of a beep test which acted as the fatiguing protocol for this study; and the last segment where the runner was required to complete the 400m run at their comfortable pace, this time in their fatigued state. The beep test requires the runner to continuously run between two points 20m apart following an audio which produces `beeps' indicating when the person should begin running from one end to the other. The test eventually requires the runner to increase their pace as the interval between the `beeps' reduces as the test progresses. The fatiguing protocol ends when the runner is unable to keep up the increase in pace. The runs were all done on an outdoor running track. The sensor captured acceleration, angular velocity and magnetometer data throughout the three stages of the trials at a sampling rate of 256Hz. The data included here are segmented strides from the two 400m runs of each of the 19 participants. The labels on the data represent the participant number and whether it was a fatigued stride ('F') or a not fatigued stride ('NF').<br>The data used from the sensors includes data from the accelerometer in three directions (X, Y, Z) and the gyroscope in three directions (X, Y, Z). The direction of each of the axis is relative to the sensor. Two extra signals, magnitude acceleration and magnitude gyroscope were derived from the component signals and included in the analysis.</p><p>Kindly cite one of the following papers when using this data:</p><p>B. Kathirgamanathan, B. Caulfield and P. Cunningham, "Towards Globalised Models for Exercise Classification using Inertial Measurement Units," 2023 IEEE 19th International Conference on Body Sensor Networks (BSN), Boston, MA, USA, 2023, pp. 1–4, doi: 10.1109/BSN58485.2023.10331612</p><p>B. Kathirgamanathan, T. Nguyen, G. Ifrim, B. Caulfield, P. Cunningham. Explaining Fatigue in Runners using Time Series Analysis on Wearable Sensor Data, XKDD 2023: 5th International Workshop on eXplainable Knowledge Discovery in Data Mining, ECML PKDD, 2023, <a href="http://xkdd2023.isti.cnr.it/papers/223.pdf">http://xkdd2023.isti.cnr.it/papers/223.pdf</a></p>
Automotive Lidar and Vibration: Resonance, Inertial Measurement Unit and Effects on the Point Cloud
<p>This data repository contains vibration tests of an Ouster OS1-64 lidar including a docker based python environment and documentation.</p> <p>It consists of movement data, Fotos, IMU data, pointclouds and a ground truth measurements with an Riegl VZ6000 laser scaner.</p> <p>For further details see the linked publication and the example.ipynb notebook.</p> <p><strong>Quick start</strong></p> <ul> <li> <p>download the repo</p> </li> <li> <p>unzip the archive</p> </li> <li> <p>install VS code with the remote development extension</p> </li> <li> <p>install docker desktop</p> </li> <li> <p>open the folder in a new VS code window</p> </li> <li> <p>Say "yes" to open the folder inside a docker container</p> </li> <li> <p>wait for the container to start</p> </li> <li> <p>open the example jupyter notebook</p> </li> </ul> <p><strong>Structure</strong></p> <blockquote> <pre>├── .devcontainer .... VS code devcontainer (arm64 and amd64)</pre> <pre>├── Acceleromenter_A_z_deflection .... Accelerometer data</pre> <pre>├── Foto .... Fotos of the setup</pre> <pre>│ ├── VZ6000 .... Fotos of the ground truth measurements</pre> <pre>│ └── test_setup .... Fotos of the test setup</pre> <pre>├── Notebook .... A jupyter notebook with examples</pre> <pre>├── OS1_64_IMU .... Ouster IMU data</pre> <pre>├── OS1_64_pointcloud .... point cloud data</pre> <pre>├── VZ6000_groundtruth .... Ground truth data from Riegl VZ6000</pre> <pre> └── targets .... contains 2 different sets of reference</pre> <pre>│ ├── scene_aligned_by_reflectors</pre> <pre>│ └── targets_aligned</pre> <pre>└── files.csv .... an overview of the files and meta data</pre> </blockquote>
Driving Events Dataset: a smartphone inertial measurement unit for driving events
<p>The experiments were carried out by a single driver on three trips (i.e., trips #1, #2, #3) using a 2010 Volkswagen Fox 1.0 under conditions of dry track and regular asphalts. The data were collected with a smartphone model Xiaomi Redmi Note 8 Pro.</p> <p>We obtained 169 events, subdivided into 26 non-aggressive events, 25 aggressive right-turn events, 23 aggressive left-turn events, 29 aggressive lane change events to the right, 23 aggressive lane change events to the left, 22 aggressive braking events, and 21 aggressive acceleration events. </p>
Synchronously recorded gait kinematic data with Inertial Measurement Units and a photogrammetry system for a validation assessment
<h3>Description</h3> <p>A gait database of 32 healthy adult subjects was built , volunteers were between 20 and 63 y.o. (33.64 ± 12.44) and 71.88% were females. Every individual underwent a barefoot walking test recorded simultaneously with Inertial Measurement Units (IMUs) and the photogrammetry system Vicon. The dataset contains the kinematic gait information of the hip, knee, and ankle joints in the three planes of motion: sagittal, frontal, and transversal. </p> <p>The signals recorded by the IMUs are referred to as I(t) and were captured with a sampling frequency of 50 Hz, and those recorded by the photogrammetry system are called V(t) and were captured with a sampling frequency of 100 Hz. To perform a comparative study of both systems, the V(t) signals must be resampled to 50 Hz. Then, the delay between the two signals must be corrected to align them. Finally, gait cycles can be extracted for each pair of trials following the data information provided, obtaining a pair of waveforms for each gait cycle [I(t), V(t)]. A total of 268 synchronous gait cycles [I(t), V(t)] can be recovered and analyzed in the three planes of motion per limb.</p> <h3>Data information</h3> <ul> <li><em>raw_data</em>: folder containing the 32 subjects raw kinematic signals recorded with IMUs (sampling frequency 50 Hz) and photogrammetry system (sampling frequency 100 Hz) synchronously.<br> <ul> <li>For IMUs records: <ul> <li>Z: sagittal plane.</li> <li>X: frontal plane.</li> <li>Y: transversal plane.</li> </ul> </li> <li>For photogrammetry system records: <ul> <li>X: sagittal plane.</li> <li>Y: frontal plane.</li> <li>Z: transversal plane.</li> </ul> </li> </ul> </li> </ul> <ul> <li><em>captures_information.xlsx</em>: table containing the delay correction and the samples corresponding to the events of the gait cycles. The delay correction is the number of samples for which each photogrammetry signal V(t), after being resampled to 50 Hz, must be moved to be completely aligned with its synchronous IMUs signal couple I(t). <ul> <li>If the delay is positive (+) the V(t) signal must be delayed by adding zeros at the beginning.</li> <li>If the delay is negative (-) the V(t) signal must be moved forward by removing zeros at the beginning.</li> </ul> </li> </ul>
Quantification of Error Sources with Inertial Measurement Units in Sports - Data and Matlab Scripts
<p>Inertial measurement units (IMUs) offer the possibility to capture the lower body motions of players of outdoor team sports. However, various sources of error are present when using IMUs: the definition of the body frames, the soft tissue artefact (STA) and the orientation filer. Methods to minimize these errors are currently being used without knowing their exact influence on the various sources of errors. The goal of this study was to quantify each of the sources of error of an IMU separately. An optoelectronic system was used as a golden standard. Rigid marker clusters (RMCs) were designed to construct a rigid connection between the IMU and four markers. This allowed for the separate quantification of each of the sources of error. Ten subjects performed nine different trials, varying both in type of movement and in movement intensity. The error of the definition of the body frames (10.9-18.1 deg RMSD), the STA (3.6-9.4 deg RMSD) and the error of the orientation filter (2.8- 13.1 deg RMSD) were all quantified separately. The data and code to process the data can be found in this publication.</p> <p> </p>
Strong-motion seismogeodesy by deeply coupling GNSS receivers and inertial measurement units
<p>Understanding the origin and mechanism of destructive earthquakes is predicated on the faithful recordings of their induced ground displacements near the clearly deformed epicentral regions. Global Navigation Satellite System (GNSS) receivers, such as the GPS sort, have been recognized as the best, if not the only reliable, tool to measure such large displacements. This is achieved within the GNSS receivers by continuously tracking the satellite carrier-phase signals. However, GNSS receivers are likely to lose their fidelity in recording the complete and accurate displacement waveforms in case of fierce ground motions, since their carrier-phase tracking becomes instable when strained by such persistent and high dynamic stress. We hence developed an advanced GNSS receiver architecture where the dynamic stress suffered by the carrier-phase tracking components is compensated for by an embedded inertial measurement unit consisting of one accelerometer and one gyroscope. In this case, the carrier-phase signals can be tracked steadily by GNSS receivers, and the displacement accuracy can be improved from sub-centimeter to millimeter level by about 70% when the ground accelerations reach twice the gravitational acceleration. We believe that this advanced GNSS receiver will be an excellent strong-motion seismometer recording displacements directly at a few millimeter accuracy without missing any earthquake signals, even in case of fierce ground motions.</p>
DUO-GAIT: A Gait Dataset for Walking under Dual-Task and Fatigue Conditions with Inertial Measurement Units
<p>Details of the dataset are described in<a href="https://www.nature.com/articles/s41597-023-02391-w"> this publication</a>.</p> <p>In recent years, there has been a growing interest to develop and evaluate gait analysis algorithms based on inertial measurement unit (IMU) data, which has important implications including sports, assessment of diseases, and rehabilitation. Multi-tasking and physical fatigue are two relevant aspects of daily life gait monitoring, but there is a lack of publicly available datasets to support the development and testing of methods using a mobile IMU setup. We present a dataset consisting of 6-minute walks under single- (only walking) and dual-task (walking while performing a cognitive task) conditions in non-fatigued and fatigued states from sixteen healthy adults. Especially, nine IMUs were placed on the head, chest, lower back, wrists, legs, and feet to record under each of the above-mentioned conditions. The dataset also includes a rich set of spatio-temporal gait parameters that capture the aspects of pace, symmetry, and variability, as well as additional study-related information to support further analysis. This dataset can serve as a foundation for future research on gait monitoring in free-living environments.</p> <p> </p> <p>----------</p> <p>Version History</p> <p>Version 3: Align the last few seconds of recording in raw data of sub_07, dual-task (this does not change any of the walking/exercise sensor signals or the rest of the dataset). Remove the .DS_Store files.</p> <p>Version 2: Update the license.</p> <p>Version 1: The original upload.</p>
DetectFoG : Detection of Gait Freezing Episodes in Parkinsonian Patients Using Inertial Measurement Units
ClinicalTrials.gov study NCT05822258. IPD Sharing: NO. Countries: 1. Publications: 1.
Analysis of the Psychometric Properties of Kinematic Parameters of Locomotion Measured by Inertial Measurement Units. Validation in Healthy Children and Children with Cerebral Palsy
ClinicalTrials.gov study NCT06138925. IPD Sharing: NO. Countries: 1. Publications: 0.
The Effect of Digital Rehabilitation System With Wearable Multi-IMU (Inertial Measurement Unit) Sensors on Upper Limb Functions in Children With Brain Injury
ClinicalTrials.gov study NCT02949817. IPD Sharing: NO. Countries: 1. Publications: 0.
Pose Estimation and Inertial Measurement Unit Systems for Gait Analysis in Older Adults
ClinicalTrials.gov study NCT07119944. IPD Sharing: NO. Countries: 1. Publications: 0.
Analysis of the Psychometric Properties of Kinematic Parameters of Locomotion Measured by Inertial Units. Validation in Healthy Volunteers and Stroke Patients
ClinicalTrials.gov study NCT05104645. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Prediction of Lower Extremity Injuries Using Lower Limb-worn Inertial Measurement Units
ClinicalTrials.gov study NCT07289828. IPD Sharing: NO. Countries: 1. Publications: 0.
IceBridge IMU L0 Raw Inertial Measurement Unit Data, Version 1
This data set contains Inertial Measurement Unit (IMU) readings, including latitude, longitude, altitude, velocity, pitch, roll, and true heading, taken over Antarctica using the Systron Donner Inertial MMQ-G inertial measurement unit. The data were collected by scientists working on the Investigating the Cryospheric Evolution of the Central Antarctic Plate (ICECAP) project, which is funded by the National Science Foundation (NSF) and the Natural Environment Research Council (NERC), with additional support from NASA Operation IceBridge.
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.