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.
41
datasets available to search
ShareScore release 0.7.1
Dataset results
41 results for “Inertial data”
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>
PROTECT project second RAW inertial data for pedestrian inertial localisation (ORDP initiative)
<p><strong>Contact person(s)</strong>: Enrico de Marinis</p> <p><strong>Data collector(s)</strong>: Enrico de Marinis. Fabrizio Pucci, Michele Uliana</p> <p><strong>Data curator(s)</strong>: Guido Rosi</p> <p><strong>Work package leader(s)</strong>: Fabrizio Pucci; Fabio Andreucci</p> <p><strong>Content</strong></p> <p>Inertial Measurement Unit raw data in TXT open and readable format, to be used for processing and testing the pedestrian dead reckoning algorithms by the inertial and indoor tracking scientific community.</p> <p>The raw inertial data have been collected and made publicly available in the frame of the SME Phase 2 project PROTECT (820867), co-funded by the European Commission</p> <p><strong>Experimental data</strong></p> <p>The publicly shared archive contains the following, distinct datasets:</p> <ul> <li>RawData_20200729_141819_000002_000003_007.decod.grz: collected in Pisa (Italy) with the DUNE foot-mounted sensor unit on July 29, 2020, in the frame of the project WP4 activities (system demonstration).</li> <li>RawData_20200729_143538_000002_000003_008.decod.grz; collected in Pisa (Italy) with the DUNE foot-mounted sensor unit on July 29, 2020, in the frame of the project WP4 activities (system demonstration).</li> <li>RawData_20200729_150213_000024_000024_004.decod.grz: collected in Pisa (Italy) with the DUNE foot-mounted sensor unit on July 29, 2020, in the frame of the project WP4 activities (system demonstration).</li> <li>RawData_20200729_153840_000007_000024_005.decod.grz: collected in Pisa (Italy) with the DUNE foot-mounted sensor unit on July 29, 2020, in the frame of the project WP4 activities (system demonstration).</li> <li>RawData_20200729_155152_000024_000003_010.decod.grz: collected in Pisa (Italy) with the DUNE foot-mounted sensor unit on July 29, 2020, in the frame of the project WP4 activities (system demonstration).</li> <li>RawData_20200730_113457_000024_000004_006.decod.grz: collected in Pisa (Italy) with the DUNE foot-mounted sensor unit on July 30, 2020, in the frame of the project WP4 activities (system demonstration).</li> <li>RawData_20200730_141622_000007_000007_003.decod.grz: collected in Pisa (Italy) with the DUNE foot-mounted sensor unit on July 30, 2020, in the frame of the project WP4 activities (system demonstration).</li> <li>RawData_20200730_153554_000007_000007_004.decod.grz: collected in Pisa (Italy) with the DUNE foot-mounted sensor unit on July 30, 2020, in the frame of the project WP4 activities (system demonstration).</li> <li>RawData_YYYYMMDD_HHMMSS_000007_000003_011.decod.grz: collected in Pisa (Italy) with the DUNE foot-mounted sensor unit on July 30, 2020, in the frame of the project WP4 activities (system demonstration).</li> </ul> <p><strong>Images of the experimental data</strong></p> <p>For each of the above data files, the image of the corresponding PDR (Pedestrian Dead Reckoning) processed track has been added as a geo-referenced JPG capture overlaid on the location satellite image. The image file name is the same as the corresponding data file.</p> <ul> <li>RawData_20200729_141819_000002_000003_007.decod.jpg</li> <li>RawData_20200729_143538_000002_000003_008.decod.jpg</li> <li>RawData_20200729_150213_000024_000024_004.decod.jpg</li> <li>RawData_20200729_153840_000007_000024_005.decod.jpg:</li> <li>RawData_20200729_155152_000024_000003_010.decod.jpg</li> <li>RawData_20200730_113457_000024_000004_006.decod.jpg</li> <li>RawData_20200730_141622_000007_000007_003.decod.jpg</li> <li>RawData_20200730_153554_000007_000007_004.decod.jpg</li> <li>RawData_YYYYMMDD_HHMMSS_000007_000003_011.decod.jpg</li> </ul> <p><strong>Open and Accessible Data format</strong></p> <p>The data format is the following</p> <p>gyro(x) gyro(y) gyro(z) acc(x) acc(y) acc(z) mag(x) mag(y) mag(z) temperature altitude</p> <p>x, y, z indicate the axes of the Inertial Measurement Unit</p> <p>gyro stands for the angular velocity and is in rad/s</p> <p>acc stands for the acceleration and is in m/s^2</p> <p>mag is the magnetic field and is in milligauss</p> <p>temperature is in °C</p> <p>altitude is the output of the altimeter and is expressed in meters</p> <p>All the samples, in all datasets have been recorded with a 200 Hz sampling frequency.</p>
Kuopio gait dataset: motion capture, inertial measurement and video-based sagittal-plane keypoint data from walking trials
<p>This dataset contains motion capture (3D marker trajectories, ground reaction forces and moments), inertial measurement unit (wearable Movella Xsens MTw Awinda sensors on the pelvis, both thighs, both shanks, and both feet), and sagittal-plane video (anatomical keypoints identified with the OpenPose human pose estimation algorithm) data.<br>The data is from 51 willing participants and collected in the HUMEA laboratory in the University of Eastern Finland, Kuopio, Finland, between 2022 and 2023. All trials were conducted barefoot.</p> <p>The file structure contains an Excel file containing information of the participants, data folders under each subject (numbered 01 to 51), and a MATLAB script.</p> <p>The Excel file has the following data for the participants:</p> <ul> <li><strong>ID</strong>: ID of the participants from 1 to 51</li> <li><strong>Age</strong>: age of the participant in years</li> <li><strong>Gender</strong>: biological sex as M for male, F for female</li> <li><strong>Leg</strong>: the participant's dominant leg, identified by asking which foot the participant would use to kick a football; R for right, L for left</li> <li><strong>Height</strong>: height of the participant in centimeters</li> <li><strong>Invalid_trials</strong>: list of invalid trials in the motion capture data (MOCAP) data, usually classified as such because the participant did not properly step on the middle force plate</li> <li><strong>IAD</strong>: inter-asis distance in millimeters, the distance between palpated left and right anterior superior iliac spine, measured with a caliper</li> <li><strong>Left_knee_width</strong>: width of the left knee from medial epicondyle to lateral epicondyle in millimeters, palpated and measured with a caliper</li> <li><strong>Right_knee_width</strong>: same as above for the right knee</li> <li><strong>Left_ankle width</strong>: width of the left ankle from medial malleolus to lateral malleolus in millimeters, palpated and measured with a caliper</li> <li><strong>Right_ankle_width</strong>: same as above for the right ankle</li> <li><strong>Left_thigh_length</strong>: the distance between the greater trochanter of the left femur and the lateral epicondyle of the left femur in millimeters, palpated and measured with a measuring tape</li> <li><strong>Right_thigh_length</strong>: same as above for the right thigh</li> <li><strong>Left_shank_length</strong>: the distance between the medial epicondyle of the femur and the medial malleolus of the tibia in millimeters, palpated and measured with a measuring tape</li> <li><strong>Right_shank_length</strong>: same as above for the right shank</li> <li><strong>Mass</strong>: mass in kilograms, measured on a force plate just before the walking measurements</li> <li><strong>ICD</strong>: inter-condylar distance of the knee of the dominant leg, measured from low-field MRI</li> <li><strong>Left_knee_width_mocap</strong>: distance between reflective MOCAP markers on the medial and lateral epicondyles of the knee in millimeters, measured from a static standing trial; -1 for missing (subject did not have those markers)</li> <li><strong>Right_knee_width_mocap</strong>: same as above for the right knee</li> </ul> <p>The folders under each subject (folders numbered 01 to 51) are as follows:</p> <ul> <li><strong>imu</strong>: "Raw" inertial measurement unit (IMU) data files that can be read with Xsens Device API (included in Xsens MT Manager 4.6, which may be unavailable these days, not sure). You won't need this if you use the data in the imu_extracted folder.</li> <li><strong>imu_extracted</strong>: IMU data extracted from those data files using the Xsens Device API, so you don't have to. <ul> <li>The data is saved as MATLAB structs where the fields are named as a sensor ID (e.g., "B42D48"). The sensor IDs and their corresponding IMU locations are as follows: <ul> <li>pelvis IMU: B42DA3</li> <li>right femur IMU: B42DA2</li> <li>left femur IMU: B42D4D</li> <li>right tibia IMU: B42DAE</li> <li>left tibia IMU: B42D53</li> <li>right foot IMU: B42D48</li> <li>left foot IMU: B42D51 (except for subjects 01 and 02, where left foot IMU has the ID B42D4E)</li> </ul> </li> <li>Some of the data are just zeros as they couldn't be read from these sensors, but under each sensor, the fields "calibratedAcceleration", "freeAcceleration", "time", "rotationMatrix", and "quaternion" contain usable data. <ul> <li>time: Contains time stamps of the measurement at each frame recorded at 100 Hz, so if you remove the first value from all values in the time vector and divide the result by 100, you will get the time in seconds from the beginning of the walking trial.</li> <li>calibratedAcceleration and freeAcceleration: Contain triaxial acceleration data from the accelerometers of the IMU. freeAcceleration is just calibratedAcceleration without the effect of Earth's gravitational acceleration.</li> <li>rotationMatrix: Orientations of the IMU as rotation matrices.</li> <li>quaternion: Orientations of the IMU as quaternions.</li> </ul> </li> </ul> </li> <li><strong>openpose</strong>: Trajectories of the keypoints identified from sagittal plane video frames, saved as json files. <ul> <li>The keypoints are from the BODY_25 model of OpenPose (https://cmu-perceptual-computing-lab.github.io/openpose/web/html/doc/md_doc_02_output.html).</li> <li>Each frame in the video has its own json file.</li> <li>You can use the function in the script "OpenPose_to_keypoint_table.m" in the root folder to read the keypoint trajectories and confidences of all frames in a walking trial into MATLAB tables. The function takes as argument the path to the folder containing the json files of the walking trial.</li> </ul> </li> <li>Note that some subjects (11, 14, 37, 49) do not have keypoint and IMU data.</li> </ul> <p>The folders under each subject are divided into three ZIP archives with 17 subjects each.</p> <p>The script "OpenPose_to_keypoint_table.m" is a MATLAB script for extracting keypoint trajectories and confidences from JSON files into tables in MATLAB.</p> <p><br><strong>Publication in Data in Brief</strong>: <a href="https://doi.org/10.1016/j.dib.2024.110841" target="_blank" rel="noopener">https://doi.org/10.1016/j.dib.2024.110841</a></p> <p><br><strong>Contact</strong>: Jere Lavikainen, jere.lavikainen@uef.fi</p>
Visual and inertial data for validation of gliding models of ornithopters
<p>This dataset contains data from different gliding flights with an ornithopter in low wind conditions. For each experiment, the inertial information is provided.</p> <p>Additionally, the flights have been recorded from three different points of view to track and triangulate its position. The videos are provided and the position of the camera has been determined using a Leica Total Station system with submillimeter accuracy. A sample of the 2D track of the ornithopter is provided for each video and experiment. The tracking along the three cameras are synchronized.</p> <p> </p> <p>------Camera Pose structure------</p> <p> </p> <p>Three cameras with four points: three to measure orientation and the last one the lens position. The last two points are the measured fall.<br> Camera 1 -> top left, bottom left and top right.<br> Camera 2 -> top left, top rigth and bottom right.<br> Camera 3 -> top left, bottom left and top right.<br> Then there are 14 rows. The pattern is: Point1, Point2, Point3 and Lens Position.</p> <p>------IMU structure------</p> <p>time, quaternion w, quaternion x, quaternion y, quaternion z, accelerometer x, accelerometer y, accelerometer z, Gyroscope x, Gyroscope y, Gyroscope z, magnetometer x ,magnetometer y ,magnetometer z<br> units: time->ms, accelerometer->g, gyroscope->ยบ/s</p> <p> </p>
PROTECT project RAW inertial data for pedestrian inertial localisation (ORDP initiative)
<p><strong>Content</strong></p> <p>Inertial Measurement Unit raw data in TXT open and readable format, to be used for processing and testing the pedestrian dead reckoning algorithms by the inertial and indoor tracking scientific community.</p> <p>The raw inertial data have been collected and made publicly available in the frame of the SME Phase 2 project PROTECT (820867), co-funded by the European Commission</p> <p> </p> <p><strong>Experimental data</strong></p> <p>The publicly shared archive contains the following, distinct datasets:</p> <ul> <li>RawData_5_2_20191113_180029.decod: collected in Rome (Italy) with the DUNE foot-mounted sensor unit n. 5 on November 13, 2019, in the frame of the project WP3 activities (system scale-up design); aprox. Duration, 30 mins.</li> <li>RawData_6_1_20191112_170005.decod; collected in Roma (Italy)with the DUNE foot-mounted sensor unit n. 6 on November 12, 2019, in the frame of the WP3 activities (system scale-up design), aprox. Duration, 30 mins.</li> <li>RawData_008_01_20191130_162759.decod: collected in Beijing (China) with the DUNE foot-mounted sensor unit n. 8 on November 30, 2019, in the frame of the WP4 activities (demonstration), aprox. Duration, 30 mins.</li> <li>RawData_008_02_20191201_125122.decod: collected in Beijing (China) with the DUNE foot-mounted sensor unit n. 8 on December 1<sup>st</sup>, 2019, in the frame of the WP4 activities (demonstration), aprox. Duration, 30 mins.</li> <li>RawData_008_03_20191201_153053.decod: collected in Beijing (China) with the DUNE foot-mounted sensor unit n. 8 on December 1<sup>st</sup>, 2019, in the frame of the WP4 activities (demonstration), aprox. Duration, 30 mins.</li> <li>RawData_008_06_20191120_182949.decod: collected in Roma (Italy)with the DUNE foot-mounted sensor unit n. 8 on November 20, 2019, in the frame of the WP3 activities (system scale-up design), aprox. Duration, 30 mins. The experiment has a mix of walk and fast run.</li> </ul> <p> </p> <p><strong>Open and Accessible Data format</strong></p> <p>The data format is the following</p> <p>gyro(x) gyro(y) gyro(z) acc(x) acc(y) acc(z) mag(x) mag(y) mag(z) temperature altitude</p> <p> </p> <p>x, y, z indicate the axes of the Inertial Measurement Unit</p> <p>gyro stands for the angular velocity and is in rad/s</p> <p>acc stands for the acceleration and is in m/s^2</p> <p>mag is the magnetic field and is in milligauss</p> <p>temperature is in °C</p> <p>altitude is the output of the altimeter and is expressed in meters</p> <p>All the samples, in all dataset have been recorded with a 200 Hz sampling frequency.</p>
GSTRIDE: A database of frailty and functional assessments with inertial gait data from elderly fallers and non-fallers populations
<p>The GSTRIDE database contains relevant metrics and motion data of elder people for the assessment of their health status. The data correspond to 163 patients, 45 men and 118 women, between 70 and 98 years old with an average Body Mass Index (BMI) of 26.1±5.0 kg/m<sup>2</sup> and a cognitive deterioration status index between 1 and 7, according to the Global Deterioration Scale (GDS) scale. In this way, we ensure variability among the volunteers in terms of socio-demographic and anatomic parameters and their functional and cognitive capacities. The database files are stored in CSV format to ease their usability with common data processing software.</p> <p>We provide socio-demographic data, anatomical, functional and cognitive variables, and the outcome measurements from test commonly performed for the evaluation of elder people. The evaluation tests carried out to obtain these data are the Gait Speed Test (4-metre), the Hand Grip Strength, the Short Physical Performance Battery (SPPB), the Timed up and go (TUG) and the Short Falls Efficacy Scale International (FES-I). We also include the outcomes of the GDS questionnaire, the Frailty assessment and the information about falls during the last year prior to the tests.</p> <p>These data are complemented with the gait parameters of a walking test recorded by an Inertial Measurement Unit (IMU) placed on the foot. Inertial data from foot-mounted IMUs (acceleration (m/s2), angular velocity (rad/s) and timestamps (s)) are included in the database in .csv files for each participant.</p> <p>The current version includes a new gait analysis processing conducted following the methodology described in [1].</p> <p>The complete gait analysis is included for each participant and trial in .csv files, including the gait parameters estimated for all individual steps and the gait segmentation events. The gait parameters included are: cycle duration (CD) (s), cadence (steps/min), stride length (SL) (m), path length 3D (%SL), path length 2D (%SL), stride velocity (m/s), percentage of swing (%CD), percentage of stance (%CD), percentage of stance subphases (loading, foot-flat, and pushing) (%stance), heel strike pitch (degrees), toe-off pitch (degrees), peak angle velocity (degrees/s), turning angle (degrees), and heel range of motion (ROM) (degrees). </p> <p>GSTRIDE is specially focused on, but not limited to, the study of faller and non-faller elder people. The main aim of this dataset is the availability of study these different populations. By including the results of the health evaluation tests and questionnaires and the inertial and spatio-temporal data, researchers can analyze different techniques for the identification of fallers. Moreover, this database allows the analysis of cognitive deterioration and frailty parameters of patients by the research community.</p> <p>[1] L. Ruiz-Ruiz, J. J. García-Domínguez and A. R. Jiménez, "A Novel Foot-Forward Segmentation Algorithm for Improving IMU-Based Gait Analysis," in <em>IEEE Transactions on Instrumentation and Measurement</em>, vol. 73, pp. 1-13, 2024, Art no. 4010513, doi: 10.1109/TIM.2024.3449951.</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>
Pressure and Inertial sensing drifter data for glacial hydrology flow path.
<p>Raw data for paper titled 'Topology and pressure distribution reconstruction of an englacial channel'</p> <p>The dataset consists of field measurements conducted on Austre Brøggerbreen, Ny-Ålesund, Svalbard. The dataset consists of:</p> <p>(1)Raw englacial data (6 deployments), 2019; (2) Raw supraglacial data (11 deployments), 2019; (3) Average GNSS drifter path along the supraglacial channel, 2019; (4) GNSS drifter path along the englacial channel, 2020; (5) Englacial river mapped from a satellite image.</p> <p>The experimental work was conducted between 30.06.2019 and 05.07.2019, during the period of the main spring snow melt. All drifters were recovered by hand from the river.</p>
Data for Manuscript "Deep-Water Near-Inertial Waves and Turbulence on a Continental Slope in the South China Sea during Typhoon Mangkhut (2018)"
<p>Data for manuscript "Deep-Water Near-Inertial Waves and Turbulence on a Continental Slope in the South China Sea during Typhoon Mangkhut (2018)".</p>
Replication data for: "Effectiveness of iso-inertial resistance training on eccentric and concentric power, physical performance, and risk of falls in physically active middle-older adults: a randomised controlled trial"
<p>Replication data for: "Effectiveness of iso-inertial resistance training on eccentric and concentric power, physical performance, and risk of falls in physically active middle-older adults: a randomised controlled trial"</p> <p>This folder contains 4 files:</p> <p>1) Database that contains the values for concentric and eccentric power measured with both iso-inertial and gravitational systems (Dataset_power.xlsx)</p> <p>2) Database that contains the values for the Short Physical Performance Battery (SPPB) and Get Up and Go (GUG) test (Dataset_SPPB_GUG.xlsx)</p> <p>3) R Software script used to analyse file 1 (Iso-inertial analysis_power.R)<br> <br>4) R Software script used to analyse file 2 (Iso-inertial analysis_SPPB_GUG.R)</p>
Segmenting Mechanomyography Measures of Muscle Activity Phases Using Inertial Data
<p>This dataset contains the data used in our manuscript titled "Segmenting Mechanomyography Measures of Muscle Activity Phases Using Inertial Data". Data structure is explained in the README.txt file located at the top-level of the dataset. Manuscript title in the README.txt file and contained in the title of the zip file are of a previous working title.</p> <p>Please contact corresponding author Richard B. Woodward for any questions.</p>
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>
3D kinematics and kinetics of change of direction motions reconstructed from virtual inertial sensor data through optimal control simulation
<p>This is the data belonging to the publication "Estimating 3D kinematics and kinetics from inertial sensor data through musculoskeletal movement simulations".</p> <p>This study investigated the feasibility and accuracy of reconstructing, especially change of direction motions, with a 3D full-body musculoskeletal model by tracking virtual inertial sensor data in optimal control simulations. We used the recordings of 90 trials with optical motion capture to generate marker tracking simulations from which we computed virtual inertial sensor data. Using this data, we compared inertial tracking simulations and marker tracking simulations.</p> <p>Please see the README and the publication for further details.</p>
Dataset for Vehicle Indoor Positioning in Industrial Environments with Wi-Fi, inertial, and odometry data
<p>Dataset collected in an indoor industrial environment using a mobile unit (manually pushed trolley) that resembles an industrial vehicle equipped with several sensors, namely, Wi-Fi, wheel encoder (displacement), and Inertial Measurement Unit (IMU).</p> <p>Sensors were connected to a Raspberry Pi (RPi 3B +), which collected the data from the sensors. Ground truth information was obtained with video camera pointed towards the floor, registering the times when the trolley passed by reference tags.</p> <p>List of sensors:</p> <ul> <li>4x <strong>Wi-Fi interfaces</strong>: Edimax EW7811-Un</li> <li>2x <strong>IMUs</strong>: Adafruit BNO055</li> <li>1x <strong>Absolute Encoder</strong>: US Digital A2 (attached to a wheel with a diameter of 125 mm)</li> </ul> <p>This dataset includes:</p> <ul> <li>1x <strong>Wi-Fi radio map</strong> that can be used for Wi-Fi fingerprinting.</li> <li>6x <strong>Trajectories</strong>: including sensor data + ground truth.</li> <li><strong>APs Information</strong>: list of APs in the building, including their position and transmission channel.</li> <li><strong>Floor plan:</strong> image of the building's floor plan with obstacles and non-navigable areas.</li> <li><strong>Python package</strong> provided for: <ul> <li>parsing the dataset into a data structure (Pandas dataframes).</li> <li>performing statistical analysis on the data (number of samples, time difference between consecutive samples, etc.).</li> <li>computing Dead Reckoning trajectory from a provided initial position.</li> <li>computing Wi-Fi fingerprinting position estimates.</li> <li>determining positioning error in Dead Reckoning and Wi-Fi fingerprinting.</li> <li>generating plots including the floor plan of the building, dead reckoning trajectories, and CDFs.</li> </ul> </li> </ul> <p> </p> <p>When using this dataset, please cite its data description paper:</p> <p>Silva , I.; Pendão, C.; Torres-Sospedra, J.; Moreira, A. Industrial Environment Multi-Sensor Dataset for Vehicle Indoor Tracking with Wi-Fi, Inertial and Odometry Data. <em>Data</em> <strong>2023</strong>, <em>8</em>, 157. <a href="https://doi.org/10.3390/data8100157" target="_blank" rel="noopener">https://doi.org/10.3390/data8100157</a> </p> <p> </p>
Data from: The role of outflow-layer inertial stability in governing the radial location of secondary eyewall formation in tropical cyclones
Open the record for dataset details and reuse information.
Data from: An output-null signature of inertial load in motor cortex
Open the record for dataset details and reuse information.
Research Data for FiHi: Fusion of inertial and high-resolution acoustic data for privacy-preserving human activity recognition
<h1><strong>Description</strong></h1> <div>This dataset contains information on 20 different activities collected from 15 participants (20-55 years old) using Wit-motion smart inertial sensors and Double Acoustics guitar pickups. Each participant performs these daily activities in an unrestricted environment, with each activity lasting at least 60 seconds and repeated twice.</div> <div> </div> <div>If you use the dataset in an academic work, please cite: </div> <div> </div> <div><code>@ARTICLE{10980212,</code><br><code> author={Yang, Zhe and Zhang, Ying and Li, Yanjun and Huang, Linchong and Hu, Ping and Lin, Yuexiang},</code><br><code> journal={IEEE Transactions on Instrumentation and Measurement}, </code><br><code> title={Fusion of Inertial and High-resolution Acoustic Data for Privacy-Preserving Human Activity Recognition}, </code><br><code> year={2025},</code><br><code> volume={74},</code><br><code> number={},</code><br><code> pages={1-20},</code><br><code> keywords={Human activity recognition;Sensors;Acoustics;Feature extraction;Privacy;Microphones;Biomedical monitoring;Wireless fidelity;Sensor phenomena and characterization;Sensor fusion;Human activities recognition;inertial sensing;Hi-res audio;attention mechanism},</code><br><code> doi={10.1109/TIM.2025.3565250}}</code></div> <h1><strong>DataSet Information</strong></h1> <h2><strong>1.Original_data.zip</strong></h2> <div>The data was annotated by manually reviewing the audio clips and assigning appropriate activity labels. Timestamping the inertial sensor data with the start time of the audio device recorded by the experimenter ensured correct segmentation and synchronization of the inertial and acoustic data. The total data length for</div> <div>all participants is over 10 hours.</div> <h3><strong>(1) </strong><strong>IMU</strong><strong> DATA</strong></h3> <div>The inertial data (accelerometer and gyroscope) is sampled at 100 Hz and transmitted by Bluetooth to the host computer. These reviewed and annotated original samples from 15 participants are placed in separate csv files. The arrangement of information in each csv file is:</div> <div>Col 1-3: 3D-acceleration data (g)</div> <div>Col 4-6: 3D-gyroscope data (°/s)</div> <h3><strong>(2) Audio DATA</strong></h3> <div>The acoustic data are sampled at 192 kHz by a Steinberg sound card and transmitted by USB cable to the host computer. These original samples are placed in separate wav files, with each file name containing all the necessary information regarding the contents of the file.</div> <div><strong>For example:</strong></div> <div>100801_sitting</div> <div>Participant ID (1-4 digits): 1008, Session ID (5-6 digits): 01, Activity ID: sitting.</div> <div> </div> <h2><strong>2、Processed_data.zip</strong></h2> <div>The last two columns of each file are as follows:</div> <ul> <li> <div>participant_id: such as 100101, 100102, 100201 ...... The last two digits are the Session ID, representing the two sessions from the same participant for the same activity.</div> </li> <li> <div>activity_id: Refer to the ACTIVITY SET below</div> </li> </ul> <h3><strong>(1) </strong><strong>IMU</strong><strong> DATA</strong></h3> <div>The original inertial data is individually aligned with the processed Audio data based on their start times, with any excess data rows at the end being trimmed. Then, these inertial data files are augmented with activity_id and participant_id for identification, and consolidated into a single csv file.</div> <div>The continuous motion signal is segmented into sliding windows, each with a duration of 3 seconds and a step size of 3 seconds. Given the IMU’s sampling rate of 100 Hz, each window of inertial data consists of 300 time steps, with 6 channels of information (3 axes each for accelerometer and gyroscope). Consequently, a single inertial sample is represented by a 300 × 6 dimensional matrix.</div> <h3><strong>(2) Audio DATA</strong></h3> <div>The acoustic signals are processed using the Short Time Fourier Transform (STFT) with a window length of 1024 points and an overlap of 256 points, , which generates n linear spaced frequency bins between n kHz frequency range in the frequency domain. The output contains the estimate of the short-term, time-localized frequency patterns. We examine two levels of privacy protection: 8 ∼ 96 kHz for non-speech sound and 20 ∼ 96 kHz for inaudible sound, with 88 and 76 linear spaced frequency bins, respectively.</div> <div>Under a sample rate of 192 kHz for the original acoustic signal, there are (192000 - 256)/(1024 - 256) ≈ 250 frequency features within a second, while each feature has 88 and 76 dimensions for non-speech (8∼96 kHz) and inaudible (20∼96 kHz) feature, respectively.</div> <h1><strong>ACTIVITY </strong><strong>SET</strong></h1> <div>The activityIDs and corresponding activities are listed in the following:</div> <div>0: use microwave</div> <div>1: brush teeth</div> <div>2: browse video</div> <div>3: drink water</div> <div>4: fry</div> <div>5: lie down</div> <div>6: flush</div> <div>7: go downstairs</div> <div>8: go upstairs</div> <div>9: sit</div> <div>10: stand</div> <div>11: manipulate door</div> <div>12: type</div> <div>13: jump</div> <div>14: run</div> <div>15: walk</div> <div>16: wash hands</div> <div>17: write</div> <div>18: operate light</div> <div>19: eat</div>
data for manuscript 'Two remarkable characteristics of near-inertial wave propagation in the subtropical northwestern Pacific'
<p>Subsurface mooring observational data of Typhoon Sunvn in western Pacific</p>
Experimental data: "Dynamical clustering and wetting phenomena in inertial active matter".
<p>In each zip folder, we report experimental data for shaker frequency f=90 Hz, 120 Hz, 150 Hz at different packing fraction, corresponding to different number of particles N=30, 60, 90, 120, 150, 180, 210, 240</p>
Data of "Stabilizing classical accelerometers and gyroscopes with a quantum inertial sensor"
Open the record for dataset details and reuse information.
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.