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155 results for “accelerometers”
Smartphone sensor data (accelerometer, virtual keyboard) collected in-the-wild by Parkinson's Disease patients and Healthy Controls
<p>For detailed description of the dataset see the relevant <a href="https://www.nature.com/articles/s41598-020-78418-8">journal article</a>.</p> <p>Python code for model inference and training is available <a href="https://github.com/alpapado/deep_pd">here</a>.</p> <p> </p> <p><strong>DESCRIPTION</strong></p> <p>The dataset contains accelerometer recodings and keyboard typing data contributed by Parkinson's Disease patients and Healthy Controls. Accelerometer data consists of acceleration values recorded during phone calls and typing data consist of virtual keyboard press and release timestamps. The dataset is divided into two parts: the first part, called SData, contains data from a small, medically evaluated, set of users, while the second part, called GData, contains recordings from a large body of users with self-reported PD labels.</p> <p>The dataset is organized into 5 pickle files:</p> <p>1. <strong>imu_sdata.pickle</strong>: Contains the tri-axial accelerometer recordings for the SData part of the dataset in the form of a list of python dictionaries, one for each participating subject. Accelerometer data have been pre-processed to a sampling frequency of 100Hz and come segmented into non-overlapping 5 second windows. Hence, a segment's dimension will be 500 x 3 samples.</p> <p>Sample Python code for accessing the acceleration data of a subject</p> <pre><code class="language-python">sdata = pickle.load(open('imu_sdata.pickle', 'rb')) subject_list = list(sdata.keys()) ## Data for first subject subject_data = sdata[subject_list[0]] # subject_data is a list of length 4 ## The actual data is in the last element of the list acc_segments = subject_data[-1] num_acc_sessions_for_subject = len(acc_segments) acc_segments_for_first_session = acc_segments[0] acc_segments_for_second_session = acc_segments[1] # ..etc In: print(acc_segments_for_first_session.shape) Out: (3, 500, 3) ## The first accelerometer session for this subject consists of 3 five-second segments. In: print(acc_segments_for_second_session.shape) Out: (8, 500, 3) ## The second accelerometer session for this subject consists of 8 five-second segments.</code></pre> <p>2. <strong>imu_gdata.pickle</strong>: Same layout as imu_sdata.pickle but with data ffrom GData subjects.</p> <p>3. <strong>typing_sdata.pickle</strong>: This files contains the typing data originating from the SData part of the dataset. It is a list of dictionaries with one entry per subject. The typing data are given in the form of concatenated hold time (the time elapsed between press and release of the virtual key) and flight time (the time between releasing a key and press the next) histograms, computed over 10ms bins in the range of [0, 1]s for hold time and [0, 4]s for flight time (an additional bin that contains the values in the (1, +oo) and (4, +oo) intervals is also used). So, the total length of the concatenated histogram is 1000/10 + 1 + 4000/10 + 1 = 502.</p> <p>Sample Python code for accessing the typing data of a subject:</p> <pre><code class="language-python">sdata = pickle.load(open('typing_sdata.pickle', 'rb')) subject_list = list(sdata.keys()) ## Data for first subject subject_data = sdata[subject_list[0]] ## The actual data is in the first element of the list typing_histograms = subject_data[0] num_typing_sessions_for_subject = len(typing_histograms) typing_hist_for_first_session = typing_histograms[0] typing_hist_for_second_session = typing_histograms[1] # ..etc In: print(typing_hist_for_first_session.shape) Out: (502, ) ht_hist = typing_hist_for_first_session[:101] # Hold time histogram of the session ft_hist = typing_hist_for_first_session[101:] # Flight time histogram of the session</code></pre> <p>4. <strong>typing_gdata.pickle</strong>: Same layout as typing_sdata.pickle but with data from GData subjects.</p> <p>5. <strong>subject_metadata.pickle</strong>: A list of dictionaries with one entry per subject containing demographic information. The relevant demographic fields have the following interpretation:<br> 'age': Year of birth,<br> 'gender_id': 0 indicates male, 1 indicates female<br> 'healthstatus_id': 0 indicates PD patient, 1 indicates Healthy with PD family history, 2 indicates Healthy without PD family history</p> <p>In the case of SData subjects, there is also symptom UPDRS scores from one or two medical examinations. These are ncoded in the fields med_eval_1 and med_eval_2.</p> <p> </p> <p><strong>ETHICS & FUNDING</strong></p> <p>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>).</p> <p> </p> <p><strong>CORRESPONDANCE</strong></p> <p>Any inquiries regarding this dataset should be adressed to:</p> <p>Mr. Alexandros Papadopoulos (Electrical & Computer Engineer, PhD candidate)</p> <p>Multimedia Understanding Groupmug<br> Department of Electrical & Computer Engineering<br> Aristotle University of Thessaloniki<br> University Campus, Building C, 3rd floor<br> Thessaloniki, Greece, GR54124</p> <p>Tel: +30 2310 996359, 996365 <br> Fax: +30 2310 996398<br> E-mail: alpapado@mug.ee.auth.gr</p> <p> </p> <p><br> </p> <p> </p>
Triaxial accelerometer gait dataset: foot and lower back motion during normal and metronome walking
<p><strong>This dataset contains accelerometric data collected from young and older individuals walking in a controlled environment. The data were recorded using two triaxial accelerometers, one attached to the participant's lower back and the other attached to the foot. Participants were instructed to walk back and forth along a 205-meter corridor under two different conditions:</strong></p> <p><strong>Normal walking: </strong>Participants walked at their preferred walking speed, reflecting their natural gait and pace.</p> <p><strong>Metronome walking: </strong>Participants synchronized their walking pace to a metronome set to their preferred walking cadence. This condition introduced a rhythmic element to the walking pattern, allowing for the study of gait changes when adhering to an external tempo.</p> <p><strong>Another condition was also measured to introduce a more variable and dynamic walking pattern that reflects everyday pedestrian movement in a real-world context.</strong></p> <p><strong>Free outdoor walking:</strong> Older participants engaged in approximately 5 minutes of free walking in an urban environment, navigating city streets. During this activity, only the lumbar accelerometer was used to record data. </p>
GENEActiv accelerometer file related to the #120 OxWearables / stepcount issue
<p>An example of .bin file that have an IndexError when processing.</p> <p>Consider <a title="#120 OxWearables / stepcount issue" href="https://github.com/OxWearables/stepcount/issues/120" target="_blank" rel="noopener">#120 OxWearables / stepcount issue</a> for more details.</p> <p>The .csv files are 1-second epoch conversions from the .bin file and contain <em>time</em>, <em>x</em>, <em>y</em>, <em>z</em> columns. The conversion was done by: </p> <ol> <li>reading the .bin with the <a title="GENEAread R package" href="https://www.rdocumentation.org/packages/GENEAread/" target="_blank" rel="noopener">GENEAread R package</a>.</li> <li>keeping only the time, x, y and z columns.</li> <li>saving the data.frame into a .csv file.</li> </ol> <p>The only difference between the .csv files is the column format used for the time column before saving:</p> <ul> <li>time column in XXXXXX_....csv had a string class</li> <li>time column in XXXXXT....csv had a "POSIXct" "POSIXt" class</li> </ul>
Multi-year measurements of tree motion from an accelerometer on a spruce tree near Niwot Ridge, Colorado
<p>This repository includes 12 Hz three-axis acceleration data from an accelerometer mounted to the bole of a <em>Picea engelmannii</em> (engelmann spruce) next to the C-1 Ameriflux tower at Niwot Ridge LTER, Colorado, USA. The data were recorded from November 2014 through August 2020. More information on the installation can be found in Raleigh et al. (in review, Water Resources Research).</p> <p>The data are stored in netCDF files, chunked based on the collection date when the data were downloaded from the accelerometer.</p> <p><strong>File metadata:</strong></p> <p>Filename</p> <p>GCDC_L01_Raw_Data_Niwot_TreeXX_collection_YYYYMMDD.nc</p> <p>where</p> <p>XX = tree number (01 = spruce, 02 = fir)</p> <p>YYYYMMDD = year (YYYY), month (MM), and day (DD) of data collection</p> <p> </p> <p>Each netCDF includes four variables:</p> <p>1. serial_date = time increment (fractional days), as defined by Matlab: "A serial date number represents the whole and fractional number of days from a fixed, preset date (January 0, 0000) in the proleptic ISO calendar." The serial dates are in mountain standard time (MST) with no adjustments for daylight savings.</p> <p>2. Ax = acceleration in the vertical direction (counts)</p> <p>3. Ay = acceleration in the east-west direction (counts)</p> <p>4. Az = acceleration in the north-south direction (counts)</p> <p>To convert the "counts" unit to gravitational units (g), divide Ax, Ay, and Az each by 2048, as explained in the manufacturer's user manual.</p> <p> </p> <p> </p>
Multi-year measurements of tree motion from an accelerometer on a fir tree near Niwot Ridge, Colorado
<p>This repository includes 12 Hz three-axis acceleration data from an accelerometer mounted to the bole of an <em>Abies lasiocarpa</em> (subalpine fir) next to the C-1 Ameriflux tower at Niwot Ridge LTER, Colorado, USA. The data were recorded from November 2014 through August 2020. More information on the installation can be found in Raleigh et al. (in review, Water Resources Research).</p> <p>The data are stored in netCDF files, chunked based on the collection date when the data were downloaded from the accelerometer.</p> <p><strong>File metadata:</strong></p> <p>Filename</p> <p>GCDC_L01_Raw_Data_Niwot_TreeXX_collection_YYYYMMDD.nc</p> <p>where</p> <p>XX = tree number (01 = spruce, 02 = fir)</p> <p>YYYYMMDD = year (YYYY), month (MM), and day (DD) of data collection</p> <p> </p> <p>Each netCDF includes four variables:</p> <p>1. serial_date = time increment (fractional days), as defined by Matlab: "A serial date number represents the whole and fractional number of days from a fixed, preset date (January 0, 0000) in the proleptic ISO calendar." The serial dates are in mountain standard time (MST) with no adjustments for daylight savings.</p> <p>2. Ax = acceleration in the vertical direction (counts)</p> <p>3. Ay = acceleration in the east-west direction (counts)</p> <p>4. Az = acceleration in the north-south direction (counts)</p> <p>To convert the "counts" unit to gravitational units (g), divide Ax, Ay, and Az each by 2048, as explained in the manufacturer's user manual.</p> <p> </p>
Dataset of the paper Zeitler et al. (2021) : Scale factors of the thermospheric density - a comparison of SLR and accelerometer solutions
<p>The dataset consists of two .h5 files. "Dataset_DOGSOC_GROOPS.h5" contains the 12-hour thermospheric density scale factors of the satellites Starlette, Stella, and Larets of Chapter 4.2. Each path includes a file with three columns. The first column contains the time vector in JD2000.0. The second column and third column contain the scale factor time series (unfiltered, smoothed with a 10-day moving average filter). The following scale factor time series are available:</p> <ul> <li>DOGSOC/starlette</li> <li>DOGSOC/stella</li> <li>DOGSOC/larets</li> <li>GROOPS/starlette</li> <li>GROOPS/stella</li> <li>GROOPS/larets</li> </ul> <p> </p> <p>"Dataset_SLR_ACC.h5" contains the 12-hour thermospheric density scale factors from SLR measurements (DOGS-OC) to the satellites Starlette, WESTPAC, Stella, and Larets and from accelerometer measurements of the satellites GRACE and CHAMP of Chapter 4.1. Each path includes a file with three columns. Again, the first column contains the time vector in JD2000.0, and columns 2 and 3 contain the thermospheric density scale factors (unfiltered, smoothed with a 10-day moving average fitler). The following scale factor time series are available:</p> <ul> <li>ACC/CHAMP</li> <li>ACC/GRACE</li> <li>SLR/starlette</li> <li>SLR/westpac</li> <li>SLR/stella</li> <li>SLR/larets</li> </ul> <p>Further information about the data can be found in the file "description_of_datasets_v1.txt" or in the paper Zeitler et al. (2021): Scale factors of the thermospheric density - a comparison of SLR and accelerometer solutions. Journal of Geophysical Research: Space Physics.</p>
HRV-ACC: a dataset with R-R intervals and accelerometer data for the diagnosis of psychotic disorders using a Polar H10 wearable sensor
<p><strong>ABSTRACT</strong></p> <p>The issue of diagnosing psychotic diseases, including schizophrenia and bipolar disorder, in particular, the objectification of symptom severity assessment, is still a problem requiring the attention of researchers. Two measures that can be helpful in patient diagnosis are heart rate variability calculated based on electrocardiographic signal and accelerometer mobility data. The following dataset contains data from 30 psychiatric ward patients having schizophrenia or bipolar disorder and 30 healthy persons. The duration of the measurements for individuals was usually between 1.5 and 2 hours. R-R intervals necessary for heart rate variability calculation were collected simultaneously with accelerometer data using a wearable Polar H10 device. The Positive and Negative Syndrome Scale (PANSS) test was performed for each patient participating in the experiment, and its results were attached to the dataset. Furthermore, the code for loading and preprocessing data, as well as for statistical analysis, was included on the corresponding GitHub repository.</p> <p><strong>BACKGROUND</strong></p> <p>Heart rate variability (HRV), calculated based on electrocardiographic (ECG) recordings of R-R intervals stemming from the heart's electrical activity, may be used as a biomarker of mental illnesses, including schizophrenia and bipolar disorder (BD) [Benjamin et al]. The variations of R-R interval values correspond to the heart's autonomic regulation changes [Berntson et al, Stogios et al]. Moreover, the HRV measure reflects the activity of the sympathetic and parasympathetic parts of the autonomous nervous system (ANS) [Task Force of the European Society of Cardiology the North American Society of Pacing Electrophysiology, Matusik et al]. Patients with psychotic mental disorders show a tendency for a change in the centrally regulated ANS balance in the direction of less dynamic changes in the ANS activity in response to different environmental conditions [Stogios et al]. Larger sympathetic activity relative to the parasympathetic one leads to lower HRV, while, on the other hand, higher parasympathetic activity translates to higher HRV. This loss of dynamic response may be an indicator of mental health. Additional benefits may come from measuring the daily activity of patients using accelerometry. This may be used to register periods of physical activity and inactivity or withdrawal for further correlation with HRV values recorded at the same time.</p> <p><strong>EXPERIMENTS</strong></p> <p>In our experiment, the participants were 30 psychiatric ward patients with schizophrenia or BD and 30 healthy people. All measurements were performed using a Polar H10 wearable device. The sensor collects ECG recordings and accelerometer data and, additionally, prepares a detection of R wave peaks. Participants of the experiment had to wear the sensor for a given time. Basically, it was between 1.5 and 2 hours, but the shortest recording was 70 minutes. During this time, evaluated persons could perform any activity a few minutes after starting the measurement. Participants were encouraged to undertake physical activity and, more specifically, to take a walk. Due to patients being in the medical ward, they received instruction to take a walk in the corridors at the beginning of the experiment. They were to repeat the walk 30 minutes and 1 hour after the first walk. The subsequent walks were to be slightly longer (about 3, 5 and 7 minutes, respectively). We did not remind or supervise the command during the experiment, both in the treatment and the control group. Seven persons from the control group did not receive this order and their measurements correspond to freely selected activities with rest periods but at least three of them performed physical activities during this time. Nevertheless, at the start of the experiment, all participants were requested to rest in a sitting position for 5 minutes. Moreover, for each patient, the disease severity was assessed using the PANSS test and its scores are attached to the dataset.</p> <p>The data from sensors were collected using Polar Sensor Logger application [Happonen]. Such extracted measurements were then preprocessed and analyzed using the code prepared by the authors of the experiment. It is publicly available on the GitHub repository [Książek et al].</p> <p>Firstly, we performed a manual artifact detection to remove abnormal heartbeats due to non-sinus beats and technical issues of the device (e.g. temporary disconnections and inappropriate electrode readings). We also performed anomaly detection using Daubechies wavelet transform. Nevertheless, the dataset includes raw data, while a full code necessary to reproduce our anomaly detection approach is available in the repository. Optionally, it is also possible to perform cubic spline data interpolation. After that step, rolling windows of a particular size and time intervals between them are created. Then, a statistical analysis is prepared, e.g. mean HRV calculation using the RMSSD (Root Mean Square of Successive Differences) approach, measuring a relationship between mean HRV and PANSS scores, mobility coefficient calculation based on accelerometer data and verification of dependencies between HRV and mobility scores.</p> <p><strong>DATA DESCRIPTION</strong></p> <p>The structure of the dataset is as follows. One folder, called <em>HRV_anonymized_data</em> contains values of R-R intervals together with timestamps for each experiment participant. The data was properly anonymized, i.e. the day of the measurement was removed to prevent person identification. Files concerned with patients have the name <em>treatment_X.csv</em>, where <em>X</em> is the number of the person, while files related to the healthy controls are named <em>control_Y.csv</em>, where <em>Y</em> is the identification number of the person. Furthermore, for visualization purposes, an image of the raw RR intervals for each participant is presented. Its name is <em>raw_RR_{control,treatment}_N.png</em>, where <em>N</em> is the number of the person from the control/treatment group. The collected data are raw, i.e. before the anomaly removal. The code enabling reproducing the anomaly detection stage and removing suspicious heartbeats is publicly available in the repository [Książek et al]. The structure of consecutive files collecting R-R intervals is following:</p> <table> <tbody> <tr> <td><strong>Phone timestamp</strong></td> <td><strong>RR-interval [ms]</strong></td> </tr> <tr> <td>12:43:26.538000</td> <td>651</td> </tr> <tr> <td>12:43:27.189000</td> <td>632</td> </tr> <tr> <td>12:43:27.821000</td> <td>618</td> </tr> <tr> <td>12:43:28.439000</td> <td>621</td> </tr> <tr> <td>12:43:29.060000</td> <td>661</td> </tr> <tr> <td>...</td> <td>...</td> </tr> </tbody> </table> <p>The first column contains the timestamp for which the distance between two consecutive R peaks was registered. The corresponding R-R interval is presented in the second column of the file and is expressed in milliseconds. <br> The second folder, called <em>accelerometer_anonymized_data</em> contains values of accelerometer data collected at the same time as R-R intervals. The naming convention is similar to that of the R-R interval data: <em>treatment_X.csv </em>and <em>control_X.csv</em> represent the data coming from the persons from the treatment and control group, respectively, while <em>X </em>is the identification number of the selected participant. The numbers are exactly the same as for R-R intervals. The structure of the files with accelerometer recordings is as follows:</p> <table> <tbody> <tr> <td><strong>Phone timestamp</strong></td> <td><strong>X [mg]</strong></td> <td><strong>Y [mg]</strong></td> <td><strong>Z [mg]</strong></td> </tr> <tr> <td>13:00:17.196000</td> <td>-961</td> <td>-23</td> <td>182</td> </tr> <tr> <td>13:00:17.205000</td> <td>-965</td> <td>-21</td> <td>181</td> </tr> <tr> <td>13:00:17.215000</td> <td>-966</td> <td>-22</td> <td>187</td> </tr> <tr> <td>13:00:17.225000</td> <td>-967</td> <td>-26</td> <td>193</td> </tr> <tr> <td>13:00:17.235000</td> <td>-965</td> <td>-27</td> <td>191</td> </tr> <tr> <td>...</td> <td>...</td> <td>...</td> <td>...</td> </tr> </tbody> </table> <p>The first column contains a timestamp, while the next three columns correspond to the currently registered acceleration in three axes: X, Y and Z, in milli-g unit.</p> <p>We also attached a file with the PANSS test scores (<em>PANSS.csv</em>) for all patients participating in the measurement. The structure of this file is as follows:</p> <table> <tbody> <tr> <td><strong>no_of_person</strong></td> <td><strong>PANSS_P</strong></td> <td><strong>PANSS_N</strong></td> <td><strong>PANSS_G</strong></td> <td><strong>PANSS_total</strong></td> </tr> <tr> <td>1</td> <td>8</td> <td>13</td> <td>22</td> <td>43</td> </tr> <tr> <td>2</td> <td>11</td> <td>7</td> <td>18</td> <td>36</td> </tr> <tr> <td>3</td> <td>14</td> <td>30</td> <td>44</td> <td>88</td> </tr> <tr> <td>4</td> <td>18</td> <td>13</td> <td>27</td> <td>58</td> </tr> <tr> <td>...</td> <td>...</td> <td>...</td> <td>...</td> <td>..</td> </tr> </tbody> </table> <p><br> The first column contains the identification number of the patient, while the three following columns refer to the PANSS scores related to positive, negative and general symptoms, respectively.</p> <p><strong>USAGE NOTES</strong></p> <p>All the files necessary to run the HRV and/or accelerometer data analysis are available on the GitHub repository [Książek et al]. HRV data loading, preprocessing (i.e. anomaly detection and removal), as well as the calculation of mean HRV values in terms of the RMSSD, is performed in the <em>main.py</em> file. Also, Pearson's correlation coefficients between HRV values and PANSS scores and the statistical tests (Levene's and Mann-Whitney U tests) comparing the treatment and control groups are computed. By default, a sensitivity analysis is made, i.e. running the full pipeline for different settings of the window size for which the HRV is calculated and various time intervals between consecutive windows. Preparing the heatmaps of correlation coefficients and corresponding p-values can be done by running the <em>utils_advanced_plots.py</em> file after performing the sensitivity analysis. Furthermore, a detailed analysis for the one selected set of hyperparameters may be prepared (by setting <em>sensitivity_analysis = False</em>), i.e. for 15-minute window sizes, 1-minute time intervals between consecutive windows and without data interpolation method. Also, patients taking quetiapine may be excluded from further calculations by setting <em>exclude_quetiapine = True</em> because this medicine can have a strong impact on HRV [Hattori et al].</p> <p>The accelerometer data processing may be performed using the <em>utils_accelerometer.py</em> file. In this case, accelerometer recordings are downsampled to ensure the same timestamps as for R-R intervals and, for each participant, the mobility coefficient is calculated. Then, a correlation coefficient between mean HRV values and mobility coefficient is computed. The plotting of the pure accelerometer signal may be done by running the <em>utils_loading.py </em>file.</p> <p>The comparison of age distribution between the tested groups can be made by the histogram plotted with the use of the <em>utils_basic_plots.py</em> file.</p>
Data from "Tracking the Vector Acceleration with a Hybrid Quantum Accelerometer Triad"
<p>This upload includes data shown in the figures of the Paper "Tracking the Vector Acceleration with a Hybrid Quantum Accelerometer Triad".</p>
Accelerometer and Force/Torque Sensor Measurements for Parameter and State Estimation of an Unknown Robot End Effector
<h1>Introduction</h1> <p>This dataset was created as part of a study on the development of an estimator for the contact wrench (force and torque) of an unknown robot end effector. A conference paper from this study has been submitted and accepted for the 2024 IEEE International Conference on Real-time Computing and Robotics (RCAR 2024) [1]. </p> <p>A force/torque sensor (FTS) was attached to the robot wrist, and the unknown end effector was attached to the FTS. An inertial measurement unit (IMU) was in turn attached to the end effector. The FTS measurement can be decomposed into the (1) sensor bias, (2) contact wrench, and the effects from (3) gravity, (4) inertia, (5) vibrations, and (6) noise. Estimation of the contact wrench requires that the remaining effects are compensated for. The FTS and IMU sensor biases, as well as mass and mass center of the unknown end effector, were estimated as described by Vougioukas [2]. His method requires FTS and IMU samples from 24 specific orientations of the sensors. See his paper for a description of this calibration method.</p> <p>The hardware used to generate this dataset were:</p> <ul> <li>KUKA LBR Med 14 serial robot (KUKA AG, Germany)</li> <li>ATI Gamma FTS (ATI Industrial Automation, Inc., USA)</li> <li>ATI Netbox (ATI Industrial Automation, Inc., USA)</li> <li>MPU6886 IMU (M5Stack, China) </li> <li>Arduino Mega 2580 with a W5500 Ethernet Shield </li> </ul> <h1>Method</h1> <p>The robot was used to move the end effector, FTS, and IMU such that a trajectory could be replicated with high precision and accuracy. The trajectory was a simple rotation about the FTS y-axis. This trajectory and the resulting measurements were performed three times. The sensor signals were sampled during each iteration when:</p> <ol> <li>The robot moved freely without any kind of disturbance (<strong>basline</strong>).</li> <li>The robot moved freely with gentle taps to the robot body, using a rubber hammer (<strong>vibrations</strong>).</li> <li>The robot moved with gentle taps to the body using the hammer, and with a manual force exerted on the end effector (<strong>vibrations and contact</strong>).</li> </ol> <p>The IMU signal was obtained by the Arduino Mega using I2C, and the signal was sent from the Arduino to the external PC using the ethernet shield. This setup resulted in <strong>a phase of the IMU signal by 8416 μs</strong>. This was compensated for in the offline analysis of the study on the contact wrench estimator [1]. The sensor samplig rates were different for each sensor; they were approximately 100 Hz for the robot controller (FTS orientation measurements), 700 Hz for the FTS, and 254 Hz for the IMU. The frequency for each signal can be obtained through the timestamps in the dataset.</p> <h1>Dataset</h1> <p>Each CSV file has a row which serves as the header, which labels the columns of each file. The following nomenclature of the column labels were used:</p> <p><strong>t </strong> - Timestep in microseconds. Epoch time. <br><strong>fx,</strong> <strong>fy, fz</strong> - The force components as measured by the FTS.<br><strong>tx, ty, tz </strong>- The torque components as measured by the FTS.<br><strong>ax, ay, az </strong> - The acceleration components measured by the IMU.<br><strong>gx,gy,gz </strong>- The direction of the gravitational vector in the FTS frame.<br><strong>r11, r12, r13, r21, r22, r23, r31, r32, r33 </strong>- The components of the rotation matrix that represents the FTS orientation in the world frame. (R_wf)</p> <p>The measurements from the FTS and IMU signals from the 24 orientations (as required for the calibration method described by Vougioukas [2]), are stored in <strong>0-calibration_fts-accel.csv</strong>. Additionally, the files <strong>0-steady-state_wrench.csv </strong>and <strong>0-steady-state_accel.csv</strong> contains the continuous sensor signal from the FTS and IMU, respectively, while they were at rest; these two files can be used to calculate the sensor signal variances.</p> <p>After calibration, each sensor signal was recorded independently and stored in a separate file from the other sensors. The raw (biased) values were stored. Each test iteration produced three files:</p> <ol> <li>The end effector/FTS/IMU orientation in <strong>[test_iteration]_orientation.csv</strong></li> <li>The unbiased wrench as measured by the FTS in [<strong>test iteration]_wrench.csv</strong></li> <li>The unbiased acceleration as measured by the IMU in <strong>[test_iteration]_accel.csv</strong></li> </ol> <p>The test iteration prefix for these files are: <strong>1-baseline</strong>, <strong>2-vibrations, </strong>and <strong>3-vibrations-contact, </strong>as described in the previous section "Method". To obtain the relative time between samples across the test iteration files ([]<strong>_orientation</strong>, []<strong>_wrench</strong>, and []<strong>_accel.csv</strong>), load each dataset and determine which has the earliest timestamped sample on the first row. Then, subtract this initial timestamp value from all timestamps across the files for the respective test iteration.</p> <p>Note that the IMU frame does not align with the FTS frame (<strong>_accel.csv</strong> vs <strong>_wrench.csv</strong>), the following table describes the rotation matrix R_fa which can be used to transform the acceleration measurements from the IMU frame {a} to the FTS frame {f}. </p> <p>R_fa = </p> <table> <tbody> <tr> <td>0</td> <td>-1</td> <td>0</td> </tr> <tr> <td>0</td> <td>0</td> <td>1</td> </tr> <tr> <td>-1</td> <td>0</td> <td>0</td> </tr> </tbody> </table> <h1>References</h1> <p>[1] A. Skrede, "A Linear Discrete Kalman Filter to Estimate the Contact Wrench of an Unknown Robot End Effector", Accepted for the 2024 IEEE International Conference on Real-time Computing and Robotics (RCAR), Ålesund, Norway, June 2024 </p> <p>[2] S. Vougioukas, “Bias Estimation and Gravity Compensation For Force-Torque Sensors,” in Recent Advances in Simulation, Computational Methods and Soft Computing. WSEAS Press, 2001, pp. 82–85. </p>
Accelerometer-Based Multivariate Time-Series Dataset for Calf Behavior Classification
<p><strong>AcTBeCalf Dataset Description</strong></p> <p>The AcTBeCalf dataset is a comprehensive dataset designed to support the classification of pre-weaned calf behaviors from accelerometer data. It contains detailed accelerometer readings aligned with annotated behaviors, providing a valuable resource for research in multivariate time-series classification and animal behavior analysis. The dataset includes accelerometer data collected from 30 pre-weaned Holstein Friesian and Jersey calves, housed in group pens at the Teagasc Moorepark Research Farm, Ireland. Each calf was equipped with a 3D accelerometer sensor (AX3, Axivity Ltd, Newcastle, UK) sampling at 25 Hz and attached to a neck collar from one week of birth over 13 weeks.</p> <p>This dataset encompasses 27.4 hours of accelerometer data aligned with calf behaviors, including both prominent behaviors like lying, standing, and running, as well as less frequent behaviors such as grooming, social interaction, and abnormal behaviors.</p> <p>The dataset consists of a single CSV file with the following columns:</p> <ul> <li><strong>dateTime</strong>: Timestamp of the accelerometer reading, sampled at 25 Hz.</li> <li><strong>calfid</strong>: Identification number of the calf (1-30).</li> <li><strong>accX</strong>: Accelerometer reading for the X axis (top-bottom direction)*.</li> <li><strong>accY</strong>: Accelerometer reading for the Y axis (backward-forward direction)*.</li> <li><strong>accZ</strong>: Accelerometer reading for the Z axis (left-right direction)*.</li> <li><strong>behavior</strong>: Annotated behavior based on an ethogram of 23 behaviors.</li> <li><strong>segId</strong>: Segment identification number associated with each accelerometer reading/row, representing all readings of the same behavior segment.</li> </ul> <p>* the directions are mentioned in relation to the position of the accelerometer sensor on the calf.</p> <p><strong>Code Files Description</strong></p> <p>The dataset is accompanied by several code files to facilitate the preprocessing and analysis of the accelerometer data and to support the development and evaluation of machine learning models. The main code files included in the dataset repository are:</p> <ol> <li><strong>accelerometer_time_correction.ipynb</strong>: This script corrects the accelerometer time drift, ensuring the alignment of the accelerometer data with the reference time.</li> <li><strong>shake_pattern_detector.py</strong>: This script includes an algorithm to detect shake patterns in the accelerometer signal for aligning the accelerometer time series with reference times.</li> <li><strong>aligning_accelerometer_data_with_annotations.ipynb</strong>: This notebook aligns the accelerometer time series with the annotated behaviors based on timestamps.</li> <li><strong>manual_inspection_ts_validation.ipynb</strong>: This notebook provides a manual inspection process for ensuring the accurate alignment of the accelerometer data with the annotated behaviors.</li> <li><strong>additional_ts_generation.ipynb</strong>: This notebook generates additional time-series data from the original X, Y, and Z accelerometer readings, including Magnitude, ODBA (Overall Dynamic Body Acceleration), VeDBA (Vectorial Dynamic Body Acceleration), pitch, and roll.</li> <li><strong>genSplit.py: </strong>This script provides the logic used for the generalized subject separation for machine learning model training, validation and testing.</li> <li><strong>active_inactive_classification.ipynb</strong>: This notebook details the process of classifying behaviors into active and inactive categories using a RandomForest model, achieving a balanced accuracy of 92%.</li> <li><strong>four_behv_classification.ipynb</strong>: This notebook employs the mini-ROCKET feature derivation mechanism and a RidgeClassifierCV to classify behaviors into four categories: drinking milk, lying, running, and other, achieving a balanced accuracy of 84%.</li> </ol> <p>Kindly cite one of the following papers when using this data:</p> <p>Dissanayake, O., McPherson, S. E., Allyndrée, J., Kennedy, E., Cunningham, P., & Riaboff, L. (2024). <em>Evaluating ROCKET and Catch22 features for calf behaviour classification from accelerometer data using Machine Learning models</em>. arXiv preprint arXiv:2404.18159.</p> <p>Dissanayake, O., McPherson, S. E., Allyndrée, J., Kennedy, E., Cunningham, P., & Riaboff, L. (2024). <em>Development of a digital tool for monitoring the behaviour of pre-weaned calves using accelerometer neck-collars</em>. arXiv preprint arXiv:2406.17352</p>
GENEActiv accelerometer files collected during the project entitled "Cultures et comportements alimentaires de la jeunesse dans les pays francophones du Pacifique au XXIème siècle: exemple de la Nouvelle-Calédonie" [Eng: "Eating cultures and behaviors of young people in French-speaking Pacific countries in the 21st century: the example of New Caledonia"] (anonymized version - third part)
<p><a title="GENEActiv" href="https://activinsights.com/technology/geneactiv/" target="_blank" rel="noopener">GENEActiv</a> accelerometer .csv files converted with a 1 second epoch from raw GENEActiv .bin files recorded during the project entitled "<strong>Cultures et comportements alimentaires de la jeunesse dans les pays francophones du Pacifique au XXIème siècle: exemple de la Nouvelle-Calédonie</strong>" [en: "<strong>Eating cultures and behaviors of young people in French-speaking Pacific countries in the 21st century: the example of New Caledonia</strong>"]. Devices are 60-Hz triaxial accelerometers.</p> <p>This dataset also contains <strong>participantCharacteristics.csv</strong> that povides basic information about participants and <strong>read_a_binFile_share.R</strong> that is a short R code aiming at converting and saving accelerometer data from .bin files in 1 second epoch .csv files (consider the Methods section).</p> <p>Participant characteristics: 10 to 16 years old students and some parents.</p> <p>Number of participants: 231 (206 adolescents + 25 adults).</p> <p>Year of the study: 2018 - 2019.</p> <p>Place of the study: New Caledonia.</p> <p>The accelerometer .csv files with a 1 second epoch and extracted from raw .bin files are available in open datasets:</p> <ul> <li><a title="Open dataset - first part" href="https://doi.org/10.5281/zenodo.12615468" target="_blank" rel="noopener">anonymized version - first part</a></li> <li><a title="Open dataset - second part" href="https://doi.org/10.5281/zenodo.12638746" target="_blank" rel="noopener">anonymized version - second part</a></li> <li><a title="Open dataset - third part" href="https://doi.org/10.5281/zenodo.12682660" target="_blank" rel="noopener">anonymized version - third part</a></li> </ul> <p>The accelerometer raw .bin files are available in <strong>restricted datasets</strong>:</p> <ul> <li><a title="Restricted dataset - first part" href="https://doi.org/10.5281/zenodo.11594645" target="_blank" rel="noopener">non-anonymized version - first part</a></li> <li><a title="Restricted dataset - second part" href="https://doi.org/10.5281/zenodo.12638965" target="_blank" rel="noopener">non-anonymized version - second part</a></li> <li><a title="Restricted dataset - third part" href="https://doi.org/10.5281/zenodo.12661429" target="_blank" rel="noopener">non-anonymized version - third part</a></li> </ul> <p>Other participant characteristics (age, place of living, cultural community and socio-economic status) are available in a <a title="Information associated with GENEActiv accelerometer files collected during the project entitled "Cultures et comportements alimentaires de la jeunesse dans les pays francophones du Pacifique au XXIème siècle: exemple de la Nouvelle-Calédonie" [en: "Eating cultures and behaviors of young people in French-speaking Pacific countries in the 21st century: the example of New Caledonia"] (non-anonymized information version)" href="https://doi.org/10.5281/zenodo.12195186" target="_blank" rel="noopener">restricted non-anonymized dataset</a>.</p> <p>When using this dataset, please cite the following reference:<br><a title="Wattelez et al. 2025" href="https://doi.org/10.1016/j.dib.2024.111228" target="_blank" rel="noopener">G. Wattelez, S. Frayon, O. Galy, Assessing physical activity/behavior of adolescents living in the Pacific with accelerometer data: 231 GENEActiv records in New Caledonia, Data in Brief 58 (2025) 111228, doi: 10.1016/j.dib.2024.111228</a></p>
GENEActiv accelerometer files collected during the project entitled "Cultures et comportements alimentaires de la jeunesse dans les pays francophones du Pacifique au XXIème siècle: exemple de la Nouvelle-Calédonie" [Eng: "Eating cultures and behaviors of young people in French-speaking Pacific countries in the 21st century: the example of New Caledonia"] (anonymized version - second part)
<p><a title="GENEActiv" href="https://activinsights.com/technology/geneactiv/" target="_blank" rel="noopener">GENEActiv</a> accelerometer .csv files converted with a 1 second epoch from raw GENEActiv .bin files recorded during the project entitled "<strong>Cultures et comportements alimentaires de la jeunesse dans les pays francophones du Pacifique au XXIème siècle: exemple de la Nouvelle-Calédonie</strong>" [en: "<strong>Eating cultures and behaviors of young people in French-speaking Pacific countries in the 21st century: the example of New Caledonia</strong>"]. Devices are 60-Hz triaxial accelerometers.</p> <p>This dataset also contains <strong>participantCharacteristics.csv</strong> that povides basic information about participants and <strong>read_a_binFile_share.R</strong> that is a short R code aiming at converting and saving accelerometer data from .bin files in 1 second epoch .csv files (consider the Methods section).</p> <p>Participant characteristics: 10 to 16 years old students and some parents.</p> <p>Number of participants: 231 (206 adolescents + 25 adults).</p> <p>Year of the study: 2018 - 2019.</p> <p>Place of the study: New Caledonia.</p> <p>The accelerometer .csv files with a 1 second epoch and extracted from raw .bin files are available in open datasets:</p> <ul> <li><a title="Open dataset - first part" href="https://doi.org/10.5281/zenodo.12615468" target="_blank" rel="noopener">anonymized version - first part</a></li> <li><a title="Open dataset - second part" href="https://doi.org/10.5281/zenodo.12638746" target="_blank" rel="noopener">anonymized version - second part</a></li> <li><a title="Open dataset - third part" href="https://doi.org/10.5281/zenodo.12682660" target="_blank" rel="noopener">anonymized version - third part</a></li> </ul> <p>The accelerometer raw .bin files are available in <strong>restricted datasets</strong>:</p> <ul> <li><a title="Restricted dataset - first part" href="https://doi.org/10.5281/zenodo.11594645" target="_blank" rel="noopener">non-anonymized version - first part</a></li> <li><a title="Restricted dataset - second part" href="https://doi.org/10.5281/zenodo.12638965" target="_blank" rel="noopener">non-anonymized version - second part</a></li> <li><a title="Restricted dataset - third part" href="https://doi.org/10.5281/zenodo.12661429" target="_blank" rel="noopener">non-anonymized version - third part</a></li> </ul> <p>Other participant characteristics (age, place of living, cultural community and socio-economic status) are available in a <a title="Information associated with GENEActiv accelerometer files collected during the project entitled "Cultures et comportements alimentaires de la jeunesse dans les pays francophones du Pacifique au XXIème siècle: exemple de la Nouvelle-Calédonie" [en: "Eating cultures and behaviors of young people in French-speaking Pacific countries in the 21st century: the example of New Caledonia"] (non-anonymized information version)" href="https://doi.org/10.5281/zenodo.12195186" target="_blank" rel="noopener">restricted non-anonymized dataset</a>.</p> <p>When using this dataset, please cite the following reference:<br><a title="Wattelez et al. 2025" href="https://doi.org/10.1016/j.dib.2024.111228" target="_blank" rel="noopener">G. Wattelez, S. Frayon, O. Galy, Assessing physical activity/behavior of adolescents living in the Pacific with accelerometer data: 231 GENEActiv records in New Caledonia, Data in Brief 58 (2025) 111228, doi: 10.1016/j.dib.2024.111228</a></p>
Predicting continuous ground reaction forces from accelerometers during uphill and downhill running: A recurrent neural network solution
<p>Data and model files supporting the manuscript: </p> <p>Predicting continuous ground reaction forces from accelerometers during uphill and downhill running: A recurrent neural network solution.</p> <p>Repository: https://github.com/alcantarar/Recurrent_GRF_Prediction</p>
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>
Information and questionnaire associated with GENEActiv accelerometer files collected in Vanuatu during FALAH project (non-anonymized information)
<p>Questionnaire and additional restricted participant information of the following datasets:</p> <p><a title="GENEActiv accelerometer files collected in Vanuatu during FALAH project (non-anonymized version - first part)" href="https://doi.org/10.5281/zenodo.14043547" target="_blank" rel="noopener">GENEActiv accelerometer files collected in Vanuatu during FALAH project (non-anonymized version - first part)</a><br><a title="GENEActiv accelerometer files collected in Vanuatu during FALAH project (non-anonymized version - second part)" href="https://doi.org/10.5281/zenodo.14089527" target="_blank" rel="noopener">GENEActiv accelerometer files collected in Vanuatu during FALAH project (non-anonymized version - second part)</a><br><a title="GENEActiv accelerometer files collected in Vanuatu during FALAH project (anonymized version - first part)" href="https://doi.org/10.5281/zenodo.14043331" target="_blank" rel="noopener">GENEActiv accelerometer files collected in Vanuatu during FALAH project (anonymized version - first part)</a><br><a title="GENEActiv accelerometer files collected in Vanuatu during FALAH project (anonymized version - second part)" href="https://doi.org/10.5281/zenodo.14089477" target="_blank" rel="noopener">GENEActiv accelerometer files collected in Vanuatu during FALAH project (anonymized version - second part)</a></p> <p>Participant characteristics: 13 to 17 years old students.</p> <p>Number of participants: 72.</p> <p>Year of the study: 2023.</p> <p>Place of the study: Vanuatu.</p>
Evaluation of underfloor accelerometers through fingerprinting for indoor localization
<div><strong>Fingerprinting</strong></div> <div>Code developed to test the effectiveness of an indoor positioning system where multiple accelerometers are placed under the floor and set up to collect data. This material complements the work done for the paper "Evaluation of Underfloor Accelerometers for Enabling Location-based Services in Intelligent Environments" </div> <div>and helps readers to reproduce and validate the results presented in that paper. </div> <div> </div> <p><strong>What the code does</strong><br>The execution of the main code performs the following:<br>1. generation of sensor maps through their absolute coordinates;<br>2. noise reduction on the raw data according to the average of the stress the accelerometers are subjected at quiet;<br>3. generation of the fingerprint maps per each data set;<br>4. generation of the clean ground truth files (deleting coordinates set to zero);<br>5. computation of the n-dimensional distance between observations at a given time step and the euclidean error between the minimum distance value coordinates and the respective temporally closest ground truth ones;<br>6. same as in 5 but with intra-user fingerprint maps;<br>7. same as in 5 but with inter-user fingerprint maps;<br>8. same as in 5 but with enhanced inter-user fingerprint maps.</p> <p><strong>To run the code please read the file README.md</strong></p>
Newcastle polysomnography and accelerometer data
<p># Newcastle PSG+Accelerometer study 2015</p> <p>This data set contains 55 .bin files, 28 .txt files, and one .csv file,<br> which were collected in Newcastle upon Tyne (UK) to evaluate an <br> accelerometer-based algorithm for sleep classification.<br> The data come form a a single night polysomnography recording in <br> 28 sleep clinic patients. A description of the experimental <br> protocol can be found in this open access PLoSONE paper from 2015: <br> https://doi.org/10.1371/journal.pone.0142533.</p> <p> </p> <p>## Polysomnography</p> <p>Sleep scores derived from polysomnography are stored in the .txt files. <br> Each file represents a time series (one night) of one participant.<br> The resolution of the scoring is <br> 30 seconds. Participants are numbered. The participant number is <br> included in the file names as “mecsleep01_...”. pariticpants_info.csv is a<br> dictionary of participant number, diagnosis, age, and sex.</p> <p>## Accelerometer data</p> <p>Accelerometer data from brand GENEActiv (https://www.activinsights.com) are <br> stored in .bin files. Per participant two accelerometers were used: <br> One accelerometer on each wrist (left and right). The right wrist from <br> participant 10 is missing, hence the total number of 55 bin files. <br> The tri-axial (three axis) accelerometers were configured to record <br> at 85.7 Hertz. The accelerometer data can be read with R package <br> GENEAread https://cran.r-project.org/web/packages/GENEAread/index.html. <br> Additional information on the accelerometer can be found on the <br> manufacturers product website: <br> https://www.activinsights.com/resources-support/geneactiv/downloads-software/, <br> including a description of the binary file structure on page 27 of <br> this (pdf) file: https://49wvycy00mv416l561vrj345-wpengine.netdna-ssl.com/wp-content/uploads/2014/03/geneactiv_instruction_manual_v1.2.pdf.<br> The participant number and the body side on which the accelerometer <br> is worn are included in the file names as “MECSLEEP01_left wrist...”.</p> <p>## Participant information</p> <p>The .csv file as included in this dataset contains a dictionary <br> of the participant numbers, sleep disorder diagnosis, <br> participant age at the time of measurement, and sex.</p> <p>## Example processing</p> <p>The code we used ourselves to process this data can be found in this <br> GitHub repository: https://github.com/wadpac/psg-ncl-acc-spt-detection-eval.<br> Note that we use R package GGIR: https://cran.r-project.org/web/packages/GGIR/, <br> which calls R package GENEAread for reading the binary data.</p>
fetal movement accelerometer data
<p>This dataset contains accelerometer-recorded fetal movement data acquired with healthy volunteers (pregnant women).</p>
Accelerometer-Derived Atmospheric Density from the CHAMP and GRACE Satellites
<p>Archive of the thermospheric density dataset (including estimated uncertainties) for CHAMP and GRACE satellite accelerometer missions, first released in February of 2011, with an extension of GRACE density data in 2012. </p>
GENEActiv accelerometer files collected during the project entitled "Cultures et comportements alimentaires de la jeunesse dans les pays francophones du Pacifique au XXIème siècle: exemple de la Nouvelle-Calédonie" [Eng: "Eating cultures and behaviors of young people in French-speaking Pacific countries in the 21st century: the example of New Caledonia"] (anonymized version - first part)
<p><a title="GENEActiv" href="https://activinsights.com/technology/geneactiv/" target="_blank" rel="noopener">GENEActiv</a> accelerometer .csv files converted with a 1 second epoch from raw GENEActiv .bin files recorded during the project entitled "<strong>Cultures et comportements alimentaires de la jeunesse dans les pays francophones du Pacifique au XXIème siècle: exemple de la Nouvelle-Calédonie</strong>" [en: "<strong>Eating cultures and behaviors of young people in French-speaking Pacific countries in the 21st century: the example of New Caledonia</strong>"]. Devices are 60-Hz triaxial accelerometers.</p> <p>This dataset also contains <strong>participantCharacteristics.csv</strong> that povides basic information about participants and <strong>read_a_binFile_share.R</strong> that is a short R code aiming at converting and saving accelerometer data from .bin files in 1 second epoch .csv files (consider the Methods section).</p> <p>Participant characteristics: 10 to 16 years old students and some parents.</p> <p>Number of participants: 231 (206 adolescents + 25 adults).</p> <p>Year of the study: 2018 - 2019.</p> <p>Place of the study: New Caledonia.</p> <p>The accelerometer .csv files with a 1 second epoch and extracted from raw .bin files are available in open datasets:</p> <ul> <li><a title="Open dataset - first part" href="https://doi.org/10.5281/zenodo.12615468" target="_blank" rel="noopener">anonymized version - first part</a></li> <li><a title="Open dataset - second part" href="https://doi.org/10.5281/zenodo.12638746" target="_blank" rel="noopener">anonymized version - second part</a></li> <li><a title="Open dataset - third part" href="https://doi.org/10.5281/zenodo.12682660" target="_blank" rel="noopener">anonymized version - third part</a></li> </ul> <p>The accelerometer raw .bin files are available in <strong>restricted datasets</strong>:</p> <ul> <li><a title="Restricted dataset - first part" href="https://doi.org/10.5281/zenodo.11594645" target="_blank" rel="noopener">non-anonymized version - first part</a></li> <li><a title="Restricted dataset - second part" href="https://doi.org/10.5281/zenodo.12638965" target="_blank" rel="noopener">non-anonymized version - second part</a></li> <li><a title="Restricted dataset - third part" href="https://doi.org/10.5281/zenodo.12661429" target="_blank" rel="noopener">non-anonymized version - third part</a></li> </ul> <p>Other participant characteristics (age, place of living, cultural community and socio-economic status) are available in a <a title="Information associated with GENEActiv accelerometer files collected during the project entitled "Cultures et comportements alimentaires de la jeunesse dans les pays francophones du Pacifique au XXIème siècle: exemple de la Nouvelle-Calédonie" [en: "Eating cultures and behaviors of young people in French-speaking Pacific countries in the 21st century: the example of New Caledonia"] (non-anonymized information version)" href="https://doi.org/10.5281/zenodo.12195186" target="_blank" rel="noopener">restricted non-anonymized dataset</a>.</p> <p>When using this dataset, please cite the following reference:<br><a title="Wattelez et al. 2025" href="https://doi.org/10.1016/j.dib.2024.111228" target="_blank" rel="noopener">G. Wattelez, S. Frayon, O. Galy, Assessing physical activity/behavior of adolescents living in the Pacific with accelerometer data: 231 GENEActiv records in New Caledonia, Data in Brief 58 (2025) 111228, doi: 10.1016/j.dib.2024.111228</a></p>
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.