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80 results for “IMU”
Consensual videos of potentially re-identifiable individuals recorded at the Autonomous Driving Test Area Baden-Württemberg (raw images with location and IMU data).
<p>For the purpose of research on data intermediaries and data anonymisation, it is necessary to test these processes with realistic video data containing personal data. For this purpose, the <a href="http://treumoda.de">Treumoda</a> project, funded by the German Federal Ministry of Education and Research (BMBF), has created a dataset of different traffic scenes containing identifiable persons.</p> <p>This video data was collected at the <a href="https://taf-bw.de/">Autonomous Driving Test Area Baden-Württemberg</a>. On the one hand, it should be possible to recognise people in traffic, including their line of sight. On the other hand, it should be usable for the demonstration and evaluation of anonymisation techniques.</p> <p><strong>The legal basis for the publication of this data set the consent given by the participants as documented in the file Consent.pdf (all purposes) in accordance with Art. 6 1 (a) and Art. 9 2 (a) GDPR. Any further processing is subject to the GDPR.</strong></p> <p>We make this dataset available for non-commercial purposes such as teaching, research and scientific communication. Please note that this licence is limited by the provisions of the GDPR. Anyone downloading this data will become an independent controller of the data. This data has been collected with the consent of the identifiable individuals depicted.</p> <p>Any consensual use must take into account the purposes mentioned in the uploaded consent forms and in the privacy terms and conditions provided to the participants (see Consent.pdf). All participants consented to all three purposes, and no consent was withdrawn at the time of publication. KIT is unable to provide you with contact details for any of the participants, as we have removed all links to personal data other than that contained in the published images.</p>
Datasets and Supporting Materials for the IPIN 2023 Competition Track 4 (Foot-Mounted IMU based Positioning, offsite-online)
<p>This package contains the datasets and supplementary materials used in the IPIN 2023 Competition.</p> <p><strong>Contents:</strong><br> - IPIN2023_Track4_CallForCompetition_v2.2.pdf: Call for competition including the technical annex describing the competition</p> <p>- 01-Logfiles: This folder contains 2 files for each Trials (Testing, Scoring01, Scoring02)<br> - IPIN2023_T4_xxx.txt : data file containing ACCE, ROTA, MAGN, PRES, TEMP, GSBS, GOBS, POSI frames<br> - IPIN2023_T4_xxx_gnss_ephem.nav : for trajectory estimation.<br> <br> - 02-Supplementary_Materials: This folder contains the datasheet files of the different sensors, a static logfile of about 12 hours that can be used for sensors bias estimation (Allan Variance) and a logfile of about 1 minute that can be used to calibrate the magnetometer sensor (Magnetometer Calibration).</p> <p>- 03-Evaluation: This folder contains the scripts used to calculate the competition metric, the 75th percentile on all evaluation points. It requires Matlab Mapping Toolbox. We also provide ground truth of the 2 scoring trials as 2 MAT and KML files. It contains samples of reported estimations and the corresponding results. Just run script_Eval_IPIN2023.mat</p> <p>We provide additional information on the competition at: https://evaal.aaloa.org/2023/call-for-competition</p> <p> </p> <p><strong>Citation Policy:</strong><br> Please, cite the following works when using the datasets included in this package:</p> <p>Ortiz, M.; Zhu, N.; Ziyou L. ; Renaudin, V. Datasets and Supporting Materials for the IPIN 2023 Competition Track 4 (Foot-Mounted IMU based Positioning, offsite-online), Zenodo 2023<br> <a href="https://doi.org/10.5281/zenodo.8399764">https://doi.org/10.5281/zenodo.8399764</a></p> <p>Check the citation policy at: <a href="https://doi.org/10.5281/zenodo.8399764">https://doi.org/10.5281/zenodo.8399764</a></p> <p> </p> <p><strong>Contact:</strong><br> For any further questions about the database and this competition track, please contact:</p> <p> Miguel Ortiz (<a href="mailto:miguel.ortiz@univ-eiffel.fr">miguel.ortiz@univ-eiffel.fr</a>) at the University Gustave Eiffel, France.<br> Ni Zhu (<a href="mailto:ni.zhu@univ-eiffel.fr">ni.zhu@univ-eiffel.fr</a>) at the University Gustave Eiffel, France.</p> <p> </p> <p><strong>Acknowledgements:</strong><br> We thank Maximilian Stahlke and Christopher Mutschler at Fraunhofer ISS, as well as Joaquín Torres-Sospedra from Universidade do Minho and Francesco Potortì and Antonino Crivello from ISTI-CNR Pisa, for their support in collecting the datasets.</p> <p>We extend our appreciation to the staff at the Museum for Industrial Culture (Museum Industriekultur) for their unwavering patience and invaluable support throughout our collection days.</p>
Data from dance activties using RAZOR 9DOF IMU
<p>Triaxial magnetometer data from the RAZOR 9DOF IMU of three salsa patterns for expert, intermediate and novice dancer.</p> <p>Files are organized as follow</p> <p>Pattern #: *.dat files, *.png files and R script</p> <p>where # is 0,2,5.</p>
Realistic GPS and IMU data
<p>Recordings of both gps and imu during a Seafar vessel navigation.</p> <p>The following values come from the gnss unit:</p> <pre><code> "heading_degrees": 154.453, "rot_degrees_min": 19.9, "lat_degrees": 51.212967072, "lon_degrees": 2.995510899, "sog_kph": 3.37,</code></pre> <p>All the other values come from the IMU eg. </p> <pre><code class="language-json"> "roll_degrees": 2.78, "pitch_degrees": 0.57, "yaw_degrees": 158.5, "roll_rate_rad_min": 0.0, "pitch_rate_rad_min": 0.0, "yaw_rate_rad_min": 0.51, "accel_x_ms2": -0.00137, "accel_y_ms2": 0.00784, "accel_z_ms2": -0.00171, "yaw_rate_degrees_min": 29.2208475517</code></pre> <p>All acceleration values are gravity-compensated.</p>
giaIndoorLoc – Auto-labeled WLAN + IMU dataset generated via VI-SLAM2tag
<p>This repository holds the data that belongs to the publication:</p> <p>M. Laska, T. Schulz, J. Grottke, C. Blut and J. Blankenbach, "VI-SLAM2tag: Low-Effort Labeled Dataset Collection for Fingerprinting-Based Indoor Localization", [arXiv:2207.02668]</p> <p>which is to appear at the 2022 IPIN conference. </p> <p>It is split into the following sub-parts:<br> - giaIndoorLoc_raw: Raw data recorded via the VI-SLAM2tag android app (https://github.com/laskama/VI-SLAM2tag_app)<br> - giaIndoorLoc: Annotated dataset (generated from giaIndoorLoc_raw)<br> - evaluation_data: Raw trajectory data that is used during evaluation of labeling accuracy of VI-SLAM2tag (Control-Point + Total Station (Tachymeter))<br> - model_evaluation: Model weights of fitted models used during baseline performance section (VII-B) of paper. Required for reproducing experiments with repo (https://github.com/laskama/mCELindoorLoc)</p> <p> </p> <p>For a detailed description, please refer to the given paper and the additional github repositories that host the implementations:</p> <p>- https://github.com/laskama/VI-SLAM2tag_post</p> <p>- https://github.com/laskama/VI-SLAM2tag_app</p> <p>- https://github.com/laskama/mCELindoorLoc</p>
LiDAR-IMU Mapping Test Dataset
<p>This dataset contains two rosbag files recorded in an outdoor environment for testing LiDAR-IMU mapping systems.</p>
WatchBLoc: A smartwatch IMU and ambient BLE dataset for room-level localisation
<p><strong>WatchBLoc dataset</strong></p> <p>This dataset consists of BLE RSSIs (emitted from identical BLE beacons) and IMU recordings (3-axial acceleration, 3-axial gyroscope, 3-axial magnetometer) recorded by a Sony Smartwatch 3. All participants wore the smartwatch in their right hand, which was the dominant hand in all participants.</p> <p>The data were recorded across two environments: a real-home and a demo-home. The demo-home consists of 6 rooms: big office, small office, kitchen, bathroom, meeting room, lab room. The real-home consists of 6 rooms: kitchen, living room, bedroom, bathroom, office, and loo. However, the living room and kitchen lie in the same open-plan space, and the loo lies within the office (i.e., it is an ensuite space). These can thus be accounted as:</p> <ul> <li>the aforementioned 6 rooms</li> <li>5 rooms, namely: open-plan kitchen/living room, bedroom, bathroom, office, loo</li> <li>5 rooms, namely: kitchen, living room, bedroom, bathroom, ensuite</li> <li>4 rooms, namely: open-plan kitchen/living room, bedroom, bathroom, ensuite.</li> </ul> <p>BLE beacons were installed in each room of the above environments. A total of three BLE configurations were considered for each room; one beacon was placed in the centre of each room (denoted as "centre" in the dataset), one by the entrance of each room (denoted "doors") and one at a location in each room chosen such that the pairwise distances between the beacons from all rooms are maximised (denoted as "far").</p> <p>The smartwatch was recording IMU at 100 Hz and BLE RSSIs at 0.2 Hz and the ground truth location which the participants had to report, by tapping the appropriate room label on the watch's screen every time they were entering a new room.</p> <p>Each participant performed the experiment for approximately 1 hour continuously; with the sensor recording application active and the user instructed on how to record the ground truth location, the participants moved around the environment, performing activities that are commonly encountered in each room in their own style. Not everyone performed the exact same activities, and the ground truth activity labels were not recorded.</p> <p>A total of 11 participants, noted as user1 to user11 performed the experiment across the two environments, yielding a total of 20 recordings, noted as rec1 to rec20 in the dataset. user1 performed the experiment in both environments; three times in the demo-home (rec1,5,8) and once in the real-home (rec13,15,17,19). user11 performed the experiment only in the real-home (rec14,16,18,20) while the rest of the users performed the experiment only in the demo home, yielding one recording each.</p> <p>Note that recording rec13,15,17,19 were recorded simultaneously, but account for the four different assumptions on what constitutes a room in the "real home", as described before. The same holds for recordings rec14,16,18,20.</p> <p>The data are organised per recording and user id. Note that some users have more than one corresponding recording.</p>
IMU-based Human Activity Recognition and Payload Estimation for Low-Back Exoskeletons
<p>This repository contains the data for a payload classification algorithm. Data are already split into train, validation and test sets. 12 subjects are included in this dataset (6M+6F).</p> <table> <tbody> <tr> <td><strong>Sensor ID</strong></td> <td><strong>Position</strong></td> </tr> <tr> <td>B6</td> <td>Chest</td> </tr> <tr> <td>3B</td> <td>Right Leg</td> </tr> <tr> <td>BB</td> <td>Right Wrist</td> </tr> <tr> <td>C9</td> <td>Left Leg</td> </tr> <tr> <td>B5</td> <td>Left Wrist</td> </tr> </tbody> </table> <p> </p> <p>This dataset was collected as part of a research project at the Politecnico di Milano and used to train deep-learning algorithms.</p>
Vision-IMU Based Collaborative Control of a Blind UAV
<p><strong>This video demonstrates the concept of two UAVs in which one UAV is ‘blind’, i.e. has no sensing methods other than its onboard IMU, whereas the other UAV has a proper knowledge of its position and orientation in space. By observing a marker attached to the blind UAV, the other UAV helps the blind UAV in controlling its absolute position in space. It does so by estimating the relative pose between the two and adding this to the absolute position estimate of the ‘non-blind’ UAV. The video shows a proof-of-concept for this approach, which could be utilized in cases where an aerial manipulator needs to interact closely with feature-poor environments. </strong></p>
PassengXR VR Headset IMU Drift Data
<p>Files with headset and vehicle-based IMU orientation data, which were compared to determine the level of IMU drift (inaccuracy) in the headset over time. From the measurements reported in "PassengXR: A Low Cost Platform for Any-Car, Multi-User, Motion-Based Passenger XR Experiences" (https://dl.acm.org/doi/10.1145/3526113.3545657)</p>
Example data and scripts for: Processing IMU signals to recreate sacral trajectory during treadmill walking
<p>Example IMU data and scripts for reconstructing the trajectory of a sacral IMU and validation with motion capture data. Also contains example code of gait event detection and synchronization.</p>
A five-sensor IMU-based Parkinson's disease patient and control dataset including three activities of daily living
<p class="MsoNormal">Parkinson's disease is an often-debilitating progressive neurological condition leading to loss of motor control. This dataset contains kinematic sensor data from two groups: one containing 15 patients with Parkinson's disease, and a control group of 19 participants without any known neurological condition. Participant ages ranged from 40–85, 21 were male, and 13 were female. The participants wore five 9-axis Inertial Measurement Units (IMUs) – one on each upper arm, each lower arm, and on their head. They were asked to perform a calibration pose, followed by three activities: making toast, putting on a cardigan, and unlocking and opening a door, with each activity repeated three times. The IMUs recorded time-series acceleration and orientation data from the moment where the participant was instructed to begin the activity (inception of the idea to act), through to the activity's completion. This dataset is planned for use in intent-sensing studies for assistive device control but is also applicable for activity recognition.</p>
Phase 2 Dose-finding IMU-838 for Ulcerative Colitis
ClinicalTrials.gov study NCT03341962. IPD Sharing: UNDECIDED. Countries: 19. Publications: 1.
MRI Trial to exPlore the efficAcy and Safety of IMU-838 in Relapsing Remitting Multiple Sclerosis (EMPhASIS)
ClinicalTrials.gov study NCT03846219. IPD Sharing: Not stated. Countries: 4. Publications: 2.
A Study of IMU-131(HER-Vaxx) and Chemotherapy Compared to Chemotherapy Only in Patients With HER2 Positive Advanced Gastric Cancer
ClinicalTrials.gov study NCT02795988. IPD Sharing: NO. Countries: 7. Publications: 2.
A Study to Evaluate the Efficacy, Safety and Tolerability of IMU-838 as Addition to Investigator's Choice of Standard of Care Therapy, in Patients With Coronavirus Disease 19 (COVID-19)
ClinicalTrials.gov study NCT04379271. IPD Sharing: Not stated. Countries: 2. Publications: 1.
A five-sensor IMU-based Parkinson's disease patient and control dataset including three activities of daily living
Open the record for dataset details and reuse information.
Example data and scripts for: Processing IMU signals to recreate sacral trajectory during treadmill walking
Open the record for dataset details and reuse information.
Livorno, Urban driving, IMU pothole detection
<p><strong>Scenario description</strong>:</p> <p>Test session for connected car detecting potholes using the combination of one or more of the following sensors: smartphone, 6LoWPAN vibration sensor, IMU.<br> The information is sent to the cloud and can be sent back to other connected vehicles for warning.<br> The information is also transmitted via V2V to AD cars that can automatically adapt the speed.</p> <p><strong>Session description</strong>:</p> <p>Test session with only a connected car with IMU based pothole detector, lap of 1,9 km on the harbour's public road. Goal is to record data for the technical evaluation.</p> <p><strong>Datasets descriptions</strong>:</p> <p><strong>AUTOPILOT_Livorno_UrbanDriving_Vehicle_all</strong>: Data generated from the vehicle sensors</p> <p>This dataset refers to the vehicle datasets generated from the vehicle sensors during Urban Driving in Livorno. This includes the data coming from the CAN bus and GPS. It includes following kind of dataset: Vehicle: general data (speed, battery); PositioningSystem: data from GPS; VehicleDynamics: data about dynamic (acceleration...); LateralControl: steering and lane control data</p> <p><strong>AUTOPILOT_Livorno_UrbanDriving_V2X_all</strong>: V2V messages during platooning sessions</p> <p>This dataset refers to the V2V messages exchanged between ITS stations (vehicles and RSUs) during the Urban Drining in Livorno.</p> <p><strong>AUTOPILOT_Livorno_UrbanDriving_IoT_all</strong>: Data extracted from IoT oneM2M platform</p> <p>This dataset refers to messages exchanged by Urban Driving devices, applications and services across the oneM2M platform.</p>
Datasets and Supporting Materials for the IPIN 2019 Competition Track 4 (Foot-Mounted IMU based Positioning, off-site)
<p>This package contains the datasets and supplementary materials used in the IPIN 2019 Competition (Pisa , Italy).</p> <p><strong>Contents:</strong></p> <ol> <li>track4_ipin2019competition.pdf: Call for competition including the technical annex describing the competition </li> <li>01-Logfiles: This folder contains 2 zip files.<br> - HKB08.zip : for sensors bias estimation.<br> - HKB21.zip : for trajectory estimation.<br> Each archive contains 4 files :<br> - HKBxx_mag.csv : magnetometer data<br> - HKBxx_sti.csv : inertial data<br> - HKBxx_ublox.ubx : GNSS data<br> - HKBxx_INFO.txt : info file<br> see track4_ipin2019competition.pdf for more details.</li> <li>02-Supplementary_Materials: This folder contains the datasheet files of the different sensors.</li> <li>03-Evaluation: This folder contains the scripts used to calculate the competition metric, the 75th percentile on all evaluation points. The ground truth is provided csv file.</li> </ol> <p><strong>Please, cite the following works when using the datasets included in this package:</strong></p> <ul> <li>Ortiz, M.; Perul, J.; Torres-Sospedra, J. Renaudin, V. Datasets and Supporting Materials for the IPIN 2019 Competition Track 4 (Foot-Mounted IMU based Positioning, off-site), Zenodo 2019 <a href="http://dx.doi.org/10.5281/zenodo.3937220">http://dx.doi.org/10.5281/zenodo.3937220</a></li> </ul> <p><strong>Additional information can be found at:</strong></p> <ul> <li><a href="http://ipin-conference.org/2019/competition.html">http://ipin-conference.org/2019/competition.html</a></li> </ul> <p><strong>For any further questions about the database and this competition track, please contact to: </strong></p> <ul> <li> <p>Miguel Ortiz (<a href="mailto:miguel.ortiz@univ-eiffel.fr">miguel.ortiz@univ-eiffel.fr</a>) at the University Gustave Eiffel, France.</p> <p> </p> </li> </ul>
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