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15 results for “gyroscope”
Physical Exercise Measurements with Accelerometer and Gyroscope
<p><span>The data represent records from an accelerometer and gyroscope during seven movement tasks that were standardized by a protocol. These tasks were performed consecutively, and each had to be completed as quickly as possible. Data are obtained from 81 children aged 9-11 years.</span></p> <p><span>Description of movement tasks (performed in following order):</span></p> <p><strong><span>4x10 m Run</span></strong><span>: The track is defined by two cones placed at a distance of 10 meters from each other. The cones must have a height of at least 20 cm. It is advisable to secure the cones to the floor with adhesive tape. It is also recommended to mark the correct placement of the cones on the floor with tape. After the start, an individual begins to run towards the opposite cone, circles it from the left, and continues back to the starting cone. This cone is circled from the right. The run continues back and forth with a touch of the opposite cone. On the final run, the individual must cross over the mark where they started and then accelerate, but more freely, to the start of the second section.</span></p> <p><strong><span>Crawling</span></strong><span>: Immediately after starting the section, the individual lies down on the ground and crawls forward, with the entire torso needing to remain constantly on the mat. The total length of the course is 10 meters. The task ends at most 1 meter before the finish line (marked as No. 3 in the image). Crawling on all fours is not acceptable.</span></p> <p><strong><span>Progressive Cone Shuttle Run</span></strong><span>: The track is marked by a series of five cones placed in a line one after the other. It is advisable to secure the cones to the ground with tape. It is also recommended to mark the correct placement of the cones on the floor with tape. The first cone (marked as 'A' – "return cone") is 1 meter away from the start, the second cone is 2 meters away. Each subsequent cone is always 1 meter away from the previous one (marked 'first' to 'fourth'). After the start, the participant runs to the first cone, touches it, and runs back to the return cone A. In this manner, from cone A, the participant runs progressively to cones two through four, returning to cone A each time. The participant must always touch each cone with one hand. This way, the length of the run progressively increases by 1 meter. After completing the run to the fourth cone, the participant circles outside all the cones and runs to the next station (marked as station 4). All runs are performed on the outside of the cones.</span></p> <p><strong><span>Ring Collection and Placement Run:</span></strong><span> The track is marked by six equally sized sections of a Swedish ladder and one cone located 10 meters away from the start of the section. The individual sections are placed alternately on the left and right side of the line between the start and the end cone, each touching with one corner of the section. Inside each section, at its centre, rings are loosely placed. Immediately after the start, the participant runs and progressively moves through all the sections towards the cone, collecting rings. After circling the cone, the participant returns along the same path, placing the rings back into each section. It's important to emphasize that the rings must not be thrown but placed on the ground. From the last section, the participant runs directly to the next station, number 5.</span></p> <p><strong><span>10x Position Change Drill</span></strong><span>: The task is performed on a mat. The station is directly in the centre of the mat. Immediately after starting the section, the individual performs 10 cycles of position changes. One cycle consists of transitioning from standing upright to lying on the stomach, rolling over onto the back, and transitioning back to an upright standing position. When performing the cycles, it is necessary to ensure that each position is correctly maintained. After completing the last cycle, the individual immediately moves on to the next station, number 6.</span></p> <p><strong><span>10x Gymnastic Hoops Passing</span></strong><span>: Immediately after starting the section, the individual performs 10 movement cycles, where one cycle consists of crawling through a gymnastic hoop from a standing position, moving from the head to the feet. At the end of each cycle, the individual always lies on the ground, then steps out of it and stands upright. Only after this does the individual lean towards the hoop again and begin a new cycle. Immediately after the last cycle, there is a run to station number 7.</span></p> <p><strong><span>10x Bench Jumping</span></strong><span> (pushing off with one foot): Immediately after starting the section, the individual runs to the right edge (outer side) of the bench at its beginning and starts jumping over the bench back and forth. The jump is made from one foot, landing on the other. Between jumps, it is necessary to touch the ground with both feet. The jumps must be performed in a way that the individual continuously moves forward and does not jump in place. After completing the last jump, the individual immediately runs to the final finish mark, where a triple hit to the back is also performed. (identical to station number 2)</span></p> <p><strong><span>Crawling</span></strong><span>: This section is carried out according to the same instructions and at the same location where the previous crawling (section 2) took place. The end of the section is at station number 3, which is also the finish line of the entire course.</span></p> <p> </p> <p>The main data file is physical_exercise.csv. Two figures are attached describing the protocol:</p> <p><span>Figure 1: Protocol – Spatial delineation for movement task execution (Protocol_physical_activities_measurements.png)</span></p> <p><span>Figure 2: Protocol – Location and sequence of movement tasks (Protocol_physical_activities.png)</span></p> <p> </p> <p>Definition of measurement conditions and settings:</p> <table> <tbody> <tr> <td>The measurement device</td> <td>Axivity AX6 device (Axivity Ltd, United Kingdom)</td> </tr> <tr> <td>Recording frequency</td> <td>100 Hz with a range of ±16 G for accelerometer and ±2000 °/s for gyroscope</td> </tr> <tr> <td>Device orientation</td> <td>x, y, and z axes determine the mediolateral, craniocau-<br>dal, and sagittal directions, respectively</td> </tr> <tr> <td>Device positioning on the body</td> <td>on the back between upper angles of the shoulder blades</td> </tr> </tbody> </table> <p> </p>
Gyroscope sensor data of shank motion during normal and barefoot walking
<p>The measurements were performed in closed and disturbance free space, where an unobstructed 10 m walkway was arranged. All tests were performed on a hard floor surface first with shoes that were adapt for walking (indoor sports shoes, sneakers etc.) and afterwards walking barefooted the same protocol. The subjects had clothing which did not restrict lower limb movement. In each test, the 10 m walking was repeated three times. Prior to testing, the procedure was demonstrated, and the sensors were carefully positioned to correct locations. The subjects were instructed to walk with their own natural walking velocity and to begin each 10 m walk from a completely stationary position.</p>
Example of dataset - Gyroscope - z axis
<p>Figure depicting the time series for the z axis of the sensor (gyroscope), which is located on the leg most affected by Parkinson's disease (PD). GFOG+ represents a volunteer with Parkinson's disease who has the FOG symptom, GFOG- represents a volunteer with Parkinson's disease who does not have the FOG symptom, and GC represents an age-matched healthy volunteer. FOG stands for gait freezing. Each task was completed during the OFF period (12 hours without PD medication) and the ON period (with PD medication). The graph depicts the angular velocity (rad/s) in the z axis for four tasks: voluntary stop, TUG (Timed up and Go), motor task, and dual-task.</p>
Accelerometer, gyroscope and pressure data associated with behaviors of free-ranging hawksbill sea turtles (Eretmochelys imbricata)
<p> </p> <p> </p> <div> <div> <div> <div> <div> <p>In this paper, we explored the use of transfer learning across species and taxa, employing fully convolutional neural networks to predict the behaviors of critically endangered hawksbill sea turtles from acceleration data. For this purpose, fully convolutional neural networks (V-net and U-net) were pre-trained on a dataset of green turtles (Zenodo link) and human data (Intensive Care Unit (ICU) HAR dataset, <a href="https://doi.org/10.24432/C54S4K" target="_new" rel="noreferrer">https://doi.org/10.24432/C54S4K</a>) before being fine-tuned on the hawksbill dataset. The results reveal a 8% and 4% improvement in F1-score with transfer learning from the green turtle and human datasets, respectively, compared to training the models from random weight initialization (without transfer learning). </p> </div> </div> </div> </div> </div> <p> </p> <p>The dataset comprised the raw acceleration, gyroscope and depth sequence of 6 free-ranging hawksbill sea turtles associated with the behaviors. The indiviuals were equipped with a on-board video recorder combined with an accelerometer, gyroscope, magnetometer and luminosity, temperature and depth sensors using four suction cups and an automatic release system over a two-day periods (see Jeantet et al. 2020 for details and the associated article). The accelerometer, gyroscope, magnetometer recorded at 20 Hz and the pressure, temperature and luminosity sensors at 1 Hz. The cameras were programmed to record until nightfall (6 pm) and resume at daybreak (6 am). The magnetometer, luminosity and temperature data are provided but not used in the associated study. </p> <p> </p> <div> <div> <div> <div> <div> <p>For each individual, the data collected by the devices was correlated with observed behaviors from video recordings. Unlabeled sequences, mostly comprising night recordings, were excluded, resulting in the creation of one file per day of deployment for each individual. </p> </div> </div> </div> </div> </div> <div> <div> <div> <div> <div> <p>To process the depth data and increase the sampling rate to 20 Hz, we used a linear interpolation technique. We called this new variable "Pressure_corr". Additionally, we calculated the pressure difference ("Pressure_diff") between each measuring point (originally at 1 Hz).</p> </div> </div> </div> </div> </div> <p> </p> <p>"In total, 69.7 hours of multi-sensor sequences were labelled from six different hawksbill turtles (approximately 11.6 hours of recording per individual, max = 17.8 hours, min = 6.3 hours, standard deviation = 3.6 hours). The predominant behavior observed in the videos was <em>Feeding</em>, totaling over 38.6 hours, followed by <em>Resting</em> and <em>Swimming</em>, with 19.1 hours and 7.9 hours, respectively. The other behaviors were expressed in minority (<em>Breathing</em>: 2.2 hours, <em>Gliding</em>: 1 hour, <em>Scratching</em>: 0.8 hour and <em>Other</em>: 0.1 hour). " </p> <p> </p> <p>The folder contains 10 Python matrices, each with 15 columns (AccX, AccY, AccZ, GyrX, GyrY, GyrZ, MagX, MagY, MagZ, Depth, Light, Temperature, Pressure_corr, Pressur_diff, Behavior) and a number of rows corresponding to the deployment duration. The title of each file indicates the camera number used (CC-09-XX) and the deployment day (DD-MM-YYYY), with the last digit specifying whether the matrix corresponds to the first or second day of deployment.</p> <p> </p> <p>The folder also contains two dictionaries (behInd_to_behName, behName_to_behInd) that specify the behaviors associated with each number used as a label in the Behavior column. Additionally, there is a dictionary (dico_info) that provides the names of the matrix columns and the frequence of recording.</p> <p> </p> <p> </p> <p> </p> <p> </p>
Data of "Stabilizing classical accelerometers and gyroscopes with a quantum inertial sensor"
Open the record for dataset details and reuse information.
Validation of Kinocardiography, a New Technology Measuring Cardiac Mechanical Activity Via Accelerometers and Gyroscopes
ClinicalTrials.gov study NCT04772807. IPD Sharing: UNDECIDED. Countries: 1. Publications: 4.
Raw acceleration, gyroscope and depth profiles associated with the observed behaviours of free-ranging immature green turtles in Martinique
Open the record for dataset details and reuse information.
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>
Dataset for A Synchronous Spin-Exchange Optically Pumped NMR-Gyroscope
<p>All data presented in the manuscript "A Synchronous Spin-Exchange Optically Pumped NMR-Gyroscope." Each table in the file includes all data for one figure in the manuscript.</p>
Accelerometer, gyroscope and pressure data associated with behaviors of free-ranging hawksbill sea turtles (Chelonia mydas)
<p> </p> <p>Data accompanying the paper: Jeantet, L., Vigon, V., Geiger, S., & Chevallier, D. (2021). Fully convolutional neural network: A solution to infer animal behaviours from multi-sensor data. <em>Ecological Modelling</em>, <em>450</em>, 109555.. doi : <a title="Persistent link using digital object identifier" href="https://doi.org/10.1016/j.ecolmodel.2021.109555" target="_blank" rel="noreferrer noopener">https://doi.org/10.1016/j.ecolmodel.2021.109555</a></p> <p> </p> <p>In this paper we developped a fully convolutional network, the V-Net, to automatically identify the behaviors of green turtle from acceleration, gyroscope, depth sensor data. With minimal preprocessing, we obtained a F1-score of 81.1% and a Global accuracy of 97.2%. </p> <p> </p> <p><strong>Associated Github with the V-Net script : </strong><a href="https://github.com/jeantetlorene/Vnet_seaturtle_behavior">https://github.com/jeantetlorene/Vnet_seaturtle_behavior</a></p> <p> </p> <p>The dataset comprised the raw acceleration, gyroscope and depth sequence of 13 free-ranging green turtles associated with the behaviors. The indiviuals were equipped with a on-board video recorder combined with an accelerometer, gyroscope, magnetometer and luminosity, temperature and depth sensors using four suction cups and an automatic release system over a two-day periods (see Jeantet et al. 2020 for details and the associated article). The accelerometer, gyroscope, magnetometer recorded at 20 Hz and the pressure, temperature and luminosity sensors at 1 Hz. The cameras were programmed to record until nightfall (6 pm) and resume at daybreak (6 am). The magnetometer, luminosity and temperature data are not provided in this dataset. </p> <p>For each individual, the data collected by the devices was correlated with observed behaviors from video recordings. Unlabeled sequences, primarily night recordings, were excluded, resulting in the creation of one file per day of deployment for each individual. A total of 46 behaviors were observed and are described in detail in Jeantet et al. (2020). The labels for these behaviors are found in the column "beh." The behaviors were grouped into six main categories: Breathing, Feeding, Gliding, Resting, Scratching, and Swimming. Any other observed behavior was categorized as Other. The associated labels for the categories can be found in the column "beh_merge."</p> <p>To process the depth data and increase the sampling rate to 20 Hz, we used a linear interpolation technique. We called this new variable "Pressure_corr". Additionally, we calculated the pressure difference ("Pressure_diff") between each measuring point (originally at 1 Hz).</p> <p>"In total, the green turtle dataset contained 68.6 hours of labelled sequences from 13 individuals (approximately 5.29 hours per individual, max = 14.67 hours, min = 0.96 hours, standard deviation = 3.39 hours). The predominant behavior observed in the videos was Resting, totaling over 34.3 hours, followed by Swimming and Breathing, with 22.3 hours and 5.7 hours, respectively. The other behaviors were expressed in minority (Gliding: 2.3 hours, Feeding: 1.8 hours, Scratching: 1.2 hours and Other: 1 hour). "</p> <p> </p> <p>The folder contains 16 Python matrices, each with 11 columns (AccX, AccY, AccZ, GyrX, GyrY, GyrZ, Depth, beh, beh_merge, Pressure_corr, Pressur_diff) and a number of rows corresponding to the deployment duration. The title of each file indicates the camera number used (CC-07-XX) and the deployment day (DD-MM-YYYY), with an additional number if the file was split due to unlabeled sequences.</p> <p> </p> <p>The folder also contains two dictionaries (behInd_to_behName, behName_to_behInd) that specify the behaviors associated with each number used as a label in the "beh" column. Two dictionaries (behInd_to_behName_cat, behName_to_behInd_cat) that specify the behavioral categories associated with each number used as a label in the "beh_merge" column. Additionally, there is a dictionary (dico_info) that provides the names of the matrix columns and the frequence of recording.</p> <p> </p> <p><strong>Please cite this dataset as :</strong> </p> <p>Jeantet, L., Planas-Bielsa, V., Benhamou, S., Geiger, S., Martin, J., Siegwalt, F., Lelong, P., Gresser, J., Etienne, D., Hielard, G., Arque, A., Regis, S., Lecerf, N., Frouin, C., Benhalilou, A., Murgale, C., Maillet, T., Andreani, L., Campistron, G., … Chevallier, D. (2024). Accelerometer, gyroscope and pressure data associated with behaviors of free-ranging hawksbill sea turtles (Chelonia mydas) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.11643602</p> <p> </p>
Combining Accelerometer, Gyroscope, Sound, Electrocardiography and Photoplethysmography Data in Cardiac Monitoring
ClinicalTrials.gov study NCT06422468. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Postural Effects of Being Rotated in a Whole Body Gyroscope
ClinicalTrials.gov study NCT01188161. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Comparison of Accuracy and Functional Outcome Between Robotic and Gyroscopic Assisted Surgery in Primary TKA
ClinicalTrials.gov study NCT06321952. IPD Sharing: UNDECIDED. Countries: 1. Publications: 0.
ORACLE: A Long-term Follow-up Study to Evaluate the Safety of GT005 in Participants With Geographic Atrophy Secondary to Age-related Macular Degeneration Treated in a Gyroscope-sponsored Antecedent St
ClinicalTrials.gov study NCT05481827. IPD Sharing: Not stated. Countries: 6. Publications: 0.
Balance Measures During Stance and Gait: Comparison of Gyroscopic Measurements of Trunk Sway
ClinicalTrials.gov study NCT02509455. IPD Sharing: Not stated. Countries: 1. Publications: 0.
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Allen Brain Atlas
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International Brain Laboratory public data
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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.