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25 results for “IMU Data”

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zenodo48/100

IMU data captured unobtrusively and in-the-wild by Parkinson's disease patients and healthy controls

DATASET The dataset contains IMU signals captured in-the-wild via the accelerometer sensor embedded in modern smartphones, for the purpose of detecting tremorous episodes, related to Parkinson's Disease (PD). A group of 31 PD patients and 14 Healthy controls contributed accelerometer data using their personal smartphones, for a period spanning many months.Tri-axial acceleration values were recorded automatically whenevera phone call was realized. The recording lasted for 75 seconds at the most. Each phone call thus resulted in one recorded accelerometer signal, also referred to as session. Each subject contributed a different amount of sessions depending on the number of phone calls they realized during the data collection period as well as their participation time (they were free to drop-out at any time). A detailed description of the capturing process as well as analysis results, can be found in the related research article. The data is presented as a list of python dictionaries, stored in a pickle file. Each dictionary in the list, corresponds to one subject and containes the following fields: 1. subject_id: scalar  A numerical value that uniquely identifies the subject. 2. subject_sessions: list of numpy.array A list of numpy arrays of shape (N, 4) that contains the tri-axial accelerometer sessions that the subject contributed. N denotes the total length of the session in samples (which varies from session to session) Column 0 of the array contains the timestamps of the accelerometer samples. Columns 1-3 contain the acceleration values across the x,y,z directions. 3. session_datetimes: list of datetime objects    A list of datetime objects that denote the capturing date and time of the   corresponding entries in the subject_sessions field. 4. annotation: dict  A dictionary containing the following tremor-related annotation values: * updrs16: scalar int The value related to tremor as described in item 16 of the part II of the MDS-UPDRS scale, as reported by the subject. * updrs20_right: scalar int in range [0, 4] The value related to rest tremor in the right hand as described in item 20 of the part III of the MDS-UPDRS scale, as reported by the attending neurologist. * updrs20_left: scalar int in range [0, 4] Same as above but for left hand. * updrs21_right: scalar int in range [0, 4] The value related to action/postural tremor in the right hand as described in item 21 of the part III of the MDS-UPDRS scale, as reported by the attending neurologist. * updrs21_left: scalar int in range [0, 4] Same as above but for left hand. * sp_expert: scalar int in range [0, 1] A binary tremor annotation created by a group of signal processing experts, upon visually examining the contributed signals in both time and frequency domain and taking into consideration the UDPRS scores of each subject. This was necessary due to the intermittent nature of tremor, as well as a number of considerations related to the in-the-wild nature of the data capturing process. For more details, we refer the reader to the dataset description in the related research article. A '1' value indicates that the subject has tremor. A '0' value indicates that the subject doesn't have tremor. * pd_status: scalar int in range [0, 1] A '1' value indicates that the subject is a PD patient. A '0' value indicates that the subject is a Healthy Control Note: Each annotation value refers to the subject as a whole, and not in any one session.   ETHICS & FUNDING The study during which the present dataset was collected is a multi-center study approved in each country available (for more info visit: <a href="http://www.i-prognosis.eu/?page_id=3606">http://www.i-prognosis.eu/?page_id=3606</a>). Informed consent, including permission for third-party access to pseudo-anonymised data, was obtained from all subjects prior to their engagement with the study. The work has received funding from the European Union's Horizon 2020 research and innovation programme under Grant Agreement No 690494 - i-PROGNOSIS: Intelligent Parkinson early detection guiding novel supportive interventions (<a href="http://www.i-prognosis.eu/">i-prognosis.eu</a>).   CORRESPONDANCE Any inquiries regarding this dataset should be adressed to: Mr. Alexandros Papadopoulos (Electrical & Computer Engineer, PhD candidate) Multimedia Understanding Groupmug Department of Electrical & Computer Engineering Aristotle University of Thessaloniki University Campus, Building C, 3rd floor Thessaloniki, Greece, GR54124 Tel: +30 2310 996359, 996365  Fax: +30 2310 996398 E-mail: alpapado@mug.ee.auth.gr   LICENSE This is an open access dataset, licensed under Creative Commons Attribution 4.0 International (<a href="https://creativecommons.org/licenses/by/4.0/">https://creativecommons.org/licenses/by/4.0/</a>).   WARRANTY This dataset comes without any warranty. Administrators of this dataset can not be held accountable for any damage (physical, financial or otherwise) caused by the use of this dataset.

opencc-by-4.0Oct 2019View details →

zenodo44/100

Arm gesture dataset based on IMU data captured from the Technaid human-robot interaction system

Two files with a dataset of ten different/independent hand gestures are provided (seven static gestures and three dynamic gestures). The data were generated in a IMU system with five sensors worn in the right forearm, right arm, chest, left arm and left forearm of a human. The Technaid human-robot interaction system was used to captured the data. The file "datasetStaticGestures.mat" was used to train and test a classifier whose purpose is the recognition of static gestures. On the other hand, the file "datasetDynamicGestures.mat" was used to train and test a classifier whose purpose is the recognition of dynamic gestures. The latter file contains an extra class (gesture) which represents non-gestures.

opencc-by-sa-4.0Oct 2018View details →

zenodo40/100

Wrist-mounted IMU data towards the investigation of free-living human eating behavior - the Free-living Food Intake Cycle (FreeFIC) dataset

Introduction The Free-living Food Intake Cycle (FreeFIC) dataset was created by the <a href="http://mug.ee.auth.gr">Multimedia Understanding Group</a> towards the investigation of in-the-wild eating behavior. This is achieved by recording the subjects’ meals as a small part part of their everyday life, unscripted, activities. The FreeFIC dataset contains the $3D$ acceleration and orientation velocity signals ($6$ DoF) from $22$ in-the-wild sessions provided by $12$ unique subjects. All sessions were recorded using a commercial smartwatch ($6$ using the Huawei Watch 2™ and the MobVoi TicWatch™ for the rest) while the participants performed their everyday activities. In addition, FreeFIC also contains the start and end moments of each meal session as reported by the participants. Description FreeFIC includes $22$ in-the-wild sessions that belong to $12$ unique subjects. Participants were instructed to wear the smartwatch to the hand of their preference well ahead before any meal and continue to wear it throughout the day until the battery is depleted. In addition, we followed a self-report labeling model, meaning that the ground truth is provided from the participant by documenting the start and end moments of their meals to the best of their abilities as well as the hand they wear the smartwatch on. The total duration of the $22$ recordings sums up to $112.71$ hours, with a mean duration of $5.12$ hours. Additional data statistics can be obtained by executing the provided python script stats_dataset.py. Furthermore, the accompanying python script viz_dataset.py will visualize the IMU signals and ground truth intervals for each of the recordings. Information on how to execute the Python scripts can be found below. <pre><code># The script(s) and the pickle file must be located in the same directory. # Tested with Python 3.6.4 # Requirements: Numpy, Pickle and Matplotlib # Calculate and echo dataset statistics $ python stats_dataset.py # Visualize signals and ground truth $ python viz_dataset.py</code></pre> FreeFIC is also tightly related to Food Intake Cycle (FIC), a dataset we created in order to investigate the in-meal eating behavior. More information about FIC can be found <a href="https://zenodo.org/record/4421861">here</a> and <a href="https://mug.ee.auth.gr/intake-cycle-detection/">here</a>. Publications If you plan to use the FreeFIC dataset or any of the resources found in this page, please cite our work: <pre><code>@article{kyritsis2020data, title={A Data Driven End-to-end Approach for In-the-wild Monitoring of Eating Behavior Using Smartwatches}, author={Kyritsis, Konstantinos and Diou, Christos and Delopoulos, Anastasios}, journal={IEEE Journal of Biomedical and Health Informatics}, year={2020}, publisher={IEEE}}</code></pre> <pre><code>@inproceedings{kyritsis2017automated, title={Detecting Meals In the Wild Using the Inertial Data of a Typical Smartwatch}, author={Kyritsis, Konstantinos and Diou, Christos and Delopoulos, Anastasios}, booktitle={2019 41th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)}, year={2019}, organization={IEEE}} </code></pre> Technical details We provide the FreeFIC dataset as a <a href="https://docs.python.org/3/library/pickle.html">pickle</a>. The file can be loaded using Python in the following way: <pre><code class="language-python">import pickle as pkl import numpy as np with open('./FreeFIC_FreeFIC-heldout.pkl','rb') as fh: dataset = pkl.load(fh)</code></pre> The dataset variable in the snipet above is a dictionary with $5$ keys. Namely: <ul> <li>'subject_id'</li> <li>'session_id'</li> <li>'signals_raw'</li> <li>'signals_proc'</li> <li>'meal_gt'</li> </ul> The contents under a specific key can be obtained by: <pre><code class="language-python">sub = dataset['subject_id'] # for the subject id ses = dataset['session_id'] # for the session id raw = dataset['signals_raw'] # for the raw IMU signals proc = dataset['signals_proc'] # for the processed IMU signals gt = dataset['meal_gt'] # for the meal ground truth </code></pre> The sub, ses, raw, proc and gt variables in the snipet above are lists with a length equal to $22$. Elements across all lists are aligned; e.g., the $3$rd element of the list under the 'session_id' key corresponds to the $3$rd element of the list under the 'signals_proc' key. sub: list Each element of the sub list is a scalar (integer) that corresponds to the unique identifier of the subject that can take the following values: $[1, 2, 3, 4, 13, 14, 15, 16, 17, 18, 19, 20]$. It should be emphasized that the subjects with ids $15, 16, 17, 18, 19$ and $20$ belong to the held-out part of the FreeFIC dataset (more information can be found in the publication titled "A Data Driven End-to-end Approach for In-the-wild Monitoring of Eating Behavior Using Smartwatches" by Kyritsis et al). Moreover, the subject identifier in FreeFIC is in-line with the subject identifier in the FIC dataset (more info <a href="https://zenodo.org/record/4421861">here</a> and <a href="https://mug.ee.auth.gr/intake-cycle-detection/">here</a>); i.e., FIC’s subject with id equal to $2$  is the same person as FreeFIC’s subject with id equal to $2$. ses: list Each element of this list is a scalar (integer) that corresponds to the unique identifier of the session that can range between $1$ and $5$. It should be noted that not all subjects have the same number of sessions. raw: list Each element of this list is dictionary with the 'acc' and 'gyr' keys. The data under the 'acc' key is a $N_{acc} \times 4$ numpy.ndarray that contains the timestamps in seconds (first column) and the $3D$ raw accelerometer measurements in $g$ (second, third and forth columns - representing the $x, y $ and $z$ axis, respectively). The data under the 'gyr' key is a $N_{gyr} \times 4$ numpy.ndarray that contains the timestamps in seconds (first column) and the $3D$ raw gyroscope measurements in ${degrees}/{second}$(second, third and forth columns - representing the $x, y $ and $z$ axis, respectively). All sensor streams are transformed in such a way that reflects all participants wearing the smartwatch at the same hand with the same orientation, thusly achieving data uniformity. This transformation is in par with the signals in the FIC dataset (more info <a href="https://zenodo.org/record/4421861">here</a> and <a href="https://mug.ee.auth.gr/intake-cycle-detection/">here</a>). Finally, the length of the raw accelerometer and gyroscope numpy.ndarrays is different $(N_{acc} \neq N_{gyr})$. This behavior is predictable and is caused by the Android platform. proc: list Each element of this list is an $M\times7$  numpy.ndarray that contains the timestamps, $3D$ accelerometer and gyroscope measurements for each meal. Specifically, the first column contains the timestamps in seconds, the second, third and forth columns contain the $x,y$ and $z$ accelerometer values in $g$ and the fifth, sixth and seventh columns contain the $x,y$ and $z$ gyroscope values in ${degrees}/{second}$. Unlike elements in the raw list, processed measurements (in the proc list) have a constant sampling rate of $100$ Hz and the accelerometer/gyroscope measurements are aligned with each other. In addition, all sensor streams are transformed in such a way that reflects all participants wearing the smartwatch at the same hand with the same orientation, thusly achieving data uniformity. This transformation is in par with the signals in the FIC dataset (more info <a href="https://zenodo.org/record/4421861">here</a> and <a href="https://mug.ee.auth.gr/intake-cycle-detection/">here</a>). No other preprocessing is performed on the data; e.g., the acceleration component due to the Earth's gravitational field is present at the processed acceleration measurements. The potential researcher can consult the article "A Data Driven End-to-end Approach for In-the-wild Monitoring of Eating Behavior Using Smartwatches" by Kyritsis et al. on how to further preprocess the IMU signals (i.e., smooth and remove the gravitational component). meal_gt: list Each element of this list is a $K\times2$ matrix. Each row represents the meal intervals for the specific in-the-wild session. The first column contains the timestamps of the meal start moments whereas the second one the timestamps of the meal end moments. All timestamps are in seconds. The number of meals $K$ varies across recordings (e.g., a recording exist where a participant consumed two meals). Ethics and funding Informed consent, including permission for third-party access to anonymised data, was obtained from all subjects prior to their engagement in the study. The work has received funding from the European Union's Horizon 2020 research and innovation programme under Grant Agreement No 727688 - <a href="https://bigoprogram.eu/">BigO: Big data against childhood obesity</a>. Contact Any inquiries regarding the FreeFIC dataset should be addressed to: Dr. Konstantinos KYRITSIS Multimedia Understanding Group (MUG) Department of Electrical & Computer Engineering Aristotle University of Thessaloniki University Campus, Building C, 3rd floor Thessaloniki, Greece, GR54124 Tel: +30 2310 996359, 996365  Fax: +30 2310 996398 E-mail: kokirits [at] mug [dot] ee [dot] auth [dot] gr

opencc-by-4.0Aug 2019View details →

zenodo40/100

Wrist-mounted IMU data towards the investigation of in-meal human eating behavior - the Food Intake Cycle (FIC) dataset

Introduction The Food Intake Cycle (FIC) dataset was created by the <a href="http://mug.ee.auth.gr">Multimedia Understanding Group</a> towards the investigation of in-meal eating behavior. The FIC dataset contains the triaxial acceleration and orientation velocity signals ($6$ DoF) from $21$ meal sessions provided by $12$ unique subjects. All meals were recorded in the restaurant of Aristotle University of Thessaloniki using a commercial smartwatch, the Microsoft Band $2$™ for ten out of the twenty-one meals and the Sony Smartwatch $2$™ for the remaining meals. In addition, the start and end moments of each food intake cycle as well as of each micromovement are annotated throughout the FIC dataset. Description A total of $12$ subjects were recorded while eating their launch at the university’s cafeteria. The total duration of the $21$ meals sums up to $246$ minutes, with a mean duration of $11.7$ minutes. Each participant was free to select the food of their preference, typically consisting of a starter soup, a salad, a main course and a desert. Prior to the recording, the participant was asked to wear the smartwatch to the hand that he typically uses in his everyday life to manipulate the fork and/or the spoon. A GoPro™ Hero $5$ camera was already set at the table of the participant using a small, $23$ cm in height, tripod facing the participant, including both the food tray and upper body part in it’s field of view. The purpose of video recording was to obtain ground truth data by manually annotating the IMU sequences based on the video stream. Participants were also asked to perform a clapping hand movement both at the start and end of the meal, for synchronization purposes (as this movement is distinctive in the accelerometer signal). No other instructions were given to the participants. It should be noted that the FIC dataset does not contain instances related with liquid consumption or eating without the fork, knife and spoon (e.g. eating directly with hands). The accompanying python script viz_dataset.py will visualize the IMU signals and food intake cycle (i.e., bite) ground truth intervals for each of the recordings. Information on how to execute the Python scripts can be found below. <pre><code class="language-python"># The script(s) and the pickle file must be located in the same directory. # Tested with Python 3.6.4 # Requirements: Numpy, Pickle and Matplotlib # Visualize signals and ground truth $ python viz_dataset.py</code></pre> FIC is also tightly related to FreeFIC, a dataset we created in order to investigate the in-the-wild eating behavior. More information on FreeFIC can be found <a href="https://zenodo.org/record/4421951">here</a> and <a href="https://mug.ee.auth.gr/free-food-intake-cycle-detection/">here</a>. Annotation Micromovements For all recordings, the start and end points of all $6$ micromovements of interest were manually labeled. The micromovements of interest include: <ul> <li>pick food, wrist manipulates a fork to pick food from the plate</li> <li>upwards, wrist moves upwards, towards the mouth area</li> <li>downwards, wrist moves downwards, away from the mouth area</li> <li>mouth, wrist inserts food in mouth</li> <li>no movement, wrist exhibits no movement</li> <li>other movement, every other wrist movement</li> </ul> The annotation process was performed in such a way that the start and end times of each micro-movement span the whole meal session, without overlapping each other. Food intake cycles For all recordings, we annotated the start and end points for each intake cycle (i.e. every bite). Each food intake cycle starts with a p, ends with a d and contains an m micromovement. Publications If you plan to use the FIC dataset or any of the resources found in this page, please cite our work: <pre><code>@article{kyritsis2019modeling, title={Modeling Wrist Micromovements to Measure In-Meal Eating Behavior from Inertial Sensor Data}, author={Kyritsis, Konstantinos and Diou, Christos and Delopoulos, Anastasios}, journal={IEEE journal of biomedical and health informatics}, year={2019}, publisher={IEEE}}</code></pre> <pre><code>@inproceedings{kyritsis2017food, title={Food intake detection from inertial sensors using lstm networks}, author={Kyritsis, Konstantinos and Diou, Christos and Delopoulos, Anastasios}, booktitle={International Conference on Image Analysis and Processing}, pages={411--418}, year={2017}, organization={Springer}}</code></pre> <pre><code>@inproceedings{kyritsis2017automated, title={Automated analysis of in meal eating behavior using a commercial wristband IMU sensor}, author={Kyritsis, Konstantinos and Tatli, Christina Lefkothea and Diou, Christos and Delopoulos, Anastasios}, booktitle={2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)}, pages={2843--2846}, year={2017}, organization={IEEE}}</code></pre> Technical details We provide the FIC dataset as a <a href="https://docs.python.org/3/library/pickle.html">pickle</a>. The file can be loaded using Python in the following way: <pre><code class="language-python">import pickle as pkl import numpy as np with open('./FIC.pkl','rb') as fh: dataset = pkl.load(fh)</code></pre> The dataset variable in the snipet above is a dictionary with $6$ keys. Namely: <ul> <li>'subject_id'</li> <li>'session_id'</li> <li>'signals_raw'</li> <li>'signals_proc'</li> <li>'meal_gt'</li> <li>'bite_gt'</li> </ul> The contents under a specific key can be obtained by: <pre><code>sub = dataset['subject_id'] # for the subject id ses = dataset['session_id'] # for the session id raw = dataset['signals_raw'] # for the raw IMU signals proc = dataset['signals_proc'] # for the processed IMU signals mm = dataset['mm_gt'] # for the micromovement ground truth bite = dataset['bite_gt'] # for the bite ground truth</code></pre> The sub, ses, raw, proc, mm and gt variables in the snipet above are lists with a length equal to $21$. Elements across all lists are aligned; e.g., the 3rd element of the list under the 'session_id' key corresponds to the 3rd element of the list under the 'signals_proc' key. sub: list Each element of the sub list is a scalar (integer) that corresponds to the unique identifier of the subject that can take values between $1$ and $12$. Moreover, the subject identifier in FIC is in-line with the subject identifier in the FreeFIC dataset (information available <a href="https://zenodo.org/record/4421951">here</a> and <a href="https://mug.ee.auth.gr/free-food-intake-cycle-detection/">here</a>); i.e., FIC’s subject with id equal to 2  is the same person as FreeFIC’s subject with id equal to 2. ses: list Each element of this list is a scalar (integer) that corresponds to the unique identifier of the session that can range between 1 and $3$. It should be noted that not all subjects have the same number of sessions. raw: list Each element of this list is dictionary with the 'acc', 'gyr' and 'offset' keys. The data under the 'acc' key is a $N_{acc}\times4$ numpy.ndarray that contains the timestamps in seconds (first column) and the $3D$ raw accelerometer measurements in $g$ (second, third and forth columns - representing the $x, y$ and $z$ axis, respectively). The data under the 'gyr' key is a $N_{gyr} \times 4$ numpy.ndarray that contains the timestamps in seconds (first column) and the $3D$ raw gyroscope measurements in $degrees/second$(second, third and forth columns - representing the $x, y$ and $z$ axis, respectively). All sensor streams are transformed in such a way that reflects all participants wearing the smartwatch at the same hand with the same orientation, thusly achieving data uniformity. This transformation is in par with the signals in the FreeFIC dataset (information available <a href="https://zenodo.org/record/4420039">here</a> and <a href="https://mug.ee.auth.gr/free-food-intake-cycle-detection/">here</a>). Finally, the length of the raw accelerometer and gyroscope numpy.ndarrays is different $N_{acc} \neq N_{gyr}$. This behavior is predictable and is caused by the Android/MS Band platforms. The offset key contains a float that is used to align the IMU sensor streams with the videos that were used for annotation purposes (videos are not provided). proc: list Each element of this list is an $M \times 7$  numpy.ndarray that contains the timestamps, $3D$ accelerometer and gyroscope measurements for each meal. Specifically, the first column contains the timestamps in seconds, the second, third and forth columns contain the $x,y$ and $z$ accelerometer values in $g$ and the fifth, sixth and seventh columns contain the $x, y$ and $z$ gyroscope values in $degrees/second$. Unlike elements in the raw list, processed measurements (in the proc list) have a constant sampling rate of 100 Hz and the accelerometer/gyroscope measurements are aligned with each other. In addition, all sensor streams are transformed in such a way that reflects all participants wearing the smartwatch at the same hand with the same orientation, thusly achieving data uniformity. This transformation is in par with the signals in the FreeFIC dataset (information available <a href="https://zenodo.org/record/4420039">here</a> and <a href="https://mug.ee.auth.gr/free-food-intake-cycle-detection/">here</a>). No other preprocessing is performed on the data; e.g., the acceleration component due to the Earth's gravitational field is present at the processed acceleration measurements. The potential researcher can consult the article "Modeling Wrist Micromovements to Measure In-Meal Eating Behavior from Inertial Sensor Data" by Kyritsis et al. on how to further preprocess the IMU signals (i.e., smooth and remove the gravitational component). mm: list Each element of this list is a $K \times 3$ numpy.ndarray. Each row represents a single micromovement interval. The first column contains the timestamps of the start moments in seconds, the second column the timestamps of the end moments in seconds and the third column a number representing the type of the micromovement. The identifier to micromovement mapping is provided below: $[1] \rightarrow$ no movement $[2] \rightarrow$ upwards $[3] \rightarrow$ downwards $[4] \rightarrow$ pick food $[5] \rightarrow$ mouth $[6] \rightarrow$ other movement bite: list Each element of this list is a $L\times2$ numpy.ndarray. Each row represents a single food intake event (i.e., a bite). The first column contains the start moments while the second column contains the end moments of each intake event. Both the start and end moments are provided in seconds. Ethics and funding Informed consent, including permission for third-party access to anonymised data, was obtained from all subjects prior to their engagement in the study. The work has received funding from the European Union's Horizon 2020 research and innovation programme under Grant Agreement No 727688 - <a href="https://bigoprogram.eu/">BigO: Big data against childhood obesity</a>. Contact Any inquiries regarding the FIC dataset should be addressed to: Dr. Konstantinos KYRITSIS Multimedia Understanding Group (MUG) Department of Electrical & Computer Engineering Aristotle University of Thessaloniki University Campus, Building C, 3rd floor Thessaloniki, Greece, GR54124 Tel: +30 2310 996359, 996365  Fax: +30 2310 996398 E-mail: kokirits [at] mug [dot] ee [dot] auth [dot] gr

opencc-by-4.0Sep 2017View details →

zenodo40/100

In-vehicle Sensing Datasets (e.g., GPS, IMU, and OBD data) In Florida

This data collection and distribution is supported by NSF OAC-1948066. These datasets include a total of 497 trajectory datasets over 2404 km. Each dataset includes  6DOF IMU data (e.g., triaxial acceleration and gyroscope data), GPS data (e.g., latitude, longitude, altitude, speed over ground, the number of connected satellites, Course Over Ground), and OBD data (e.g., rpm, throttle positions, accelerator positions, RPM, air temperature, etc.).  The data collection mechanism adopts the asynchronous sampling technologies that make capturing sensor data independent of the recorded signal. Therefore, datasets collected from each sensor are logged in separate files (e.g., time_obd.jsonl, time_gps.jsonl, time_obd.jsonl). By matching the time when each sensor module initiated to log data, one can aggregate/fuse multi-type in-vehicle sensing data.

opencc-by-4.0Oct 2023View details →

zenodo40/100

IMU data from walking trials on a treadmill

This dataset contains inertial measurement unit data from 10 treadmill walking trials. The subject is of legal age and wore a total of 12 Xsens MTw Awinda IMUs. More details in our publication (https://doi.org/10.7717/peerj.15097). Includes a readme file with details.

opencc-by-4.0Jan 2022View details →

zenodo40/100

IMU, magnetometer, and motion capture data from a UAV used for indoor magnetic field mapping and localization

IMU, magnetometer, and motion capture data from a UAV used for indoor magnetic field mapping and localization.  This data is split up into two folders.  "stationary_magnetometer_data" 0.1Hz samples of a RM3100 magnetometer that was kept in one position from July 2022 through February 2023. The data is partitioned into separate files for analytical convenience of our research. Each file here is a CSV with a timestamp (synchronized with chrony to a central computer) and the three components of the measured magnetic field. We did not calibrate this stationary magnetometer. "UAV_and_mocap_data" has many subfolders. Each subfolder is labeled by a date and a small description of the goals for that test segment. There is a single "EXPLANATION" file in each subfolder that gives more detail on the provided data. The data here includes the trajectory flown by the UAV, outdoor calibration data to adjust the raw magnetometer measurements, and IMU/motion capture data for each listed flight test. The EXPLANATION file should explain what trajectory was flown for each individual flight test.

opencc-by-4.0Apr 2023View details →

zenodo40/100

Consensual videos of potentially re-identifiable individuals recorded at the Autonomous Driving Test Area Baden-Württemberg (raw images with location and IMU data).

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. 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. 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. 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. 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.

opencc-by-nc-sa-4.0Apr 2023View details →

zenodo36/100

Data from dance activties using RAZOR 9DOF IMU

Triaxial magnetometer data from the RAZOR 9DOF IMU of three salsa patterns for expert, intermediate and novice dancer. Files are organized as follow Pattern #: *.dat files, *.png files and R script where # is 0,2,5.

opencc-by-sa-4.0May 2015View details →

zenodo36/100

Realistic GPS and IMU data

Recordings of both gps and imu during a Seafar vessel navigation. The following values come from the gnss unit: <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> All the other values come from the IMU eg.  <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> All acceleration values are gravity-compensated.

opencc-by-4.0May 2022View details →

zenodo36/100

PassengXR VR Headset IMU Drift Data

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)

opencc-by-4.0Apr 2024View details →

dryad36/100

Example data and scripts for: Processing IMU signals to recreate sacral trajectory during treadmill walking

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.

opencc-zeroJun 2023View details →

dryad36/100

Example data and scripts for: Processing IMU signals to recreate sacral trajectory during treadmill walking

Open the record for dataset details and reuse information.

publicJun 2023View details →

zenodo32/100

IMU data collected for motion recognition

IMU data in walking, speed varied from 0-8 km/h  IMU and FSR data in gait cycle( five gait events per cycle) IMU data in 9 different activities

opencc-by-4.0May 2023View details →

dryad28/100

Data from: Preferred gait and walk–run transition speeds in ostriches measured using GPS-IMU sensors

The ostrich (Struthio camelus) is widely appreciated as a fast and agile bipedal athlete, and is a useful comparative bipedal model for human locomotion. Here, we used GPS-IMU sensors to measure naturally selected gait dynamics of ostriches roaming freely over a wide range of speeds in an open field and developed a quantitative method for distinguishing walking and running using accelerometry. We compared freely selected gait–speed distributions with previous laboratory measures of gait dynamics and energetics. We also measured the walk–run and run–walk transition speeds and compared them with those reported for humans. We found that ostriches prefer to walk remarkably slowly, with a narrow walking speed distribution consistent with minimizing cost of transport (CoT) according to a rigid-legged walking model. The dimensionless speeds of the walk–run and run–walk transitions are slower than those observed in humans. Unlike humans, ostriches transition to a run well below the mechanical limit necessitating an aerial phase, as predicted by a compass-gait walking model. When running, ostriches use a broad speed distribution, consistent with previous observations that ostriches are relatively economical runners and have a flat curve for CoT against speed. In contrast, horses exhibit U-shaped curves for CoT against speed, with a narrow speed range within each gait for minimizing CoT. Overall, the gait dynamics of ostriches moving freely over natural terrain are consistent with previous lab-based measures of locomotion. Nonetheless, ostriches, like humans, exhibit a gait-transition hysteresis that is not explained by steady-state locomotor dynamics and energetics. Further study is required to understand the dynamics of gait transitions.

opencc-zeroDec 2015View details →

zenodo28/100

YUTO MMS Dataset, Sequence B (Lidar data, Calibration files, Groundtruth, GPS, IMU, Timestamp)

Open the record for dataset details and reuse information.

opencc-by-4.0Jan 2024View details →

dryad28/100

Data from: Preferred gait and walk–run transition speeds in ostriches measured using GPS-IMU sensors

Open the record for dataset details and reuse information.

publicAug 2016View details →

nasa28/100

IceBridge UAF GPS/IMU L1B Corrected Position and Attitude Data, Version 1

This data set contains Global Positioning System (GPS) and Inertial Measurement Unit (IMU) positioning and orientation measurements for the University of Alaska Fairbanks Glacier lidar system campaigns. The data were collected as part of Operation IceBridge funded aircraft survey campaigns.

restrictednotspecifiedApr 2025View details →

nasa24/100

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.

restrictednotspecifiedApr 2025View details →

zenodo16/100

GNSS and IMU data recorded during a vessel sail

Example data point of this data set: <pre><code>"0": { "timestamp": 1674573910407, "acceleration_x_ms2": -0.121791042, "acceleration_y_ms2": -0.025389839, "acceleration_z_ms2": -0.049855232, "gps_lat": 51.1537593172, "gps_lon": 2.8057965827, "sog_kmh": 0.39, "rot_deg_min": "1.3", "heading_deg": 230.313, "imu_lat": null, "imu_lon": null, "roll_rate_deg_min": null, "pitch_rate_deg_min": null, "yaw_rate_deg_min": null, "roll_deg": null, "pitch_deg": null, "yaw_deg": null }</code></pre>

restrictedJun 2023View details →

ScienceDex guides

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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.

Compare curated 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.

allen-brain-atlas

neuroscienceopenDocumentation, web resources, and API references are available online.

Last verified 2026-04-30Open record

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.

abode-home-cage

behavioral-neuroscienceopenThe DataShare record exposes download links for annotations, documentation, license text, and the zipped per-snippet data directory.

Last verified 2026-04-30Open record

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.

dandi-nwb

electrophysiologyopenPublished Dandiset metadata and archive endpoints are available through the production DANDI API.

Last verified 2026-04-30Open record

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.

ibl

behavioral-neuroscienceopenPublic sessions can be searched and loaded from the IBL public data server through ONE.

Last verified 2026-04-29Open record

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.

openneuro

neuroscienceopenPublished datasets are available on demand over the internet.

Last verified 2026-04-29Open record