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93 results for “wearable sensor”
Continuous multi-sensor wearable data and daily subject-reported fatigue of heathy adults
<p>Fatigue is a broad, multifactorial concept encompassing feelings of reduced physical and mental energy levels. Fatigue strongly impacts health-related quality of life across a huge range of conditions, yet, to date, tools available to understand fatigue are limited. We collected a total of 28 healthy adult subjects and 973 recording days. Recorded data included continuous multimodal wearable sensor time series on physical activity, vital signs, and other physiological parameters at 1-minute temporal resolution, and daily questionnaires (patient-reported outcome scores, PROs) on fatigue. When matching both sensor data and PROs, the datasets contains data from 27 subjects and 405 recording days.</p> <p>Analysis of these multimodal digital data to inform, quantify, and augment subjectively captured non-pathological fatigue measures were published at <em>Luo H., et. al. (2020), Assessment of Fatigue Using Wearable Sensors: A Pilot Study. Digit Biomark</em>.</p> <p>Demographics, sensor parameters and other information on this dataset can be found in the aforementioned manuscript and related supplementary material.</p> <p>Files included are</p> <ul> <li><em>fatiguePROs.csv</em>: daily PROs for all subjects</li> <li>subjectID_*.csv: sensor time series for each subject</li> </ul>
Two Wearable Sensor Datasets recording the Countermovement Jump
<p>These datasets come from two independent studies using wearable inertial sensors to estimate countermovement jump performance. The participants were healthy sports science students, free of injury, all of whom had given their prior written consent. Ethical approval was given by the governing institutions’ ethics committees, which included further analysis of the data.</p> <ul> <li><strong>Smartphone Dataset:</strong> <ul> <li>119 valid jumps</li> <li>Peak power 40.7 +/- 8.9 W/kg</li> <li>22 males, 10 females (26.5 +/- 4.1 yrs; standing height 1.74 +/- 0.08 m; body mass 70.0 +/- 10.9 kg)</li> <li>Redmi 9T phone (Xiaomi Technology, Beijing, China)</li> <li>128 Hz sampling frequency</li> <li>Accelerometer & gyroscope</li> <li>Handheld at sternum level</li> <li>Mascia, G.; De Lazzari, B.; Camomilla, V. Machine learning aided jump height estimate democratization through smartphone measures. Frontiers in Sports and Active Living 2023, 5, 1112739. <a href="https://doi.org/10.3389/fspor.2023.1112739">https://doi.org/10.3389/fspor.2023.1112739</a>.</li> </ul> </li> <li><strong>Accelerometer Dataset:</strong> <ul> <li>347 valid jumps</li> <li>Peak power 45.1 +/- 7.6 W/kg</li> <li>48 males, 25 females (21.6 +/- 3.3 yrs; standing height 1.75 +/- 0.10 m; body mass 71.2 +/- 15.1 kg)</li> <li>Trigno sensor (Delsys Inc, MA, USA)</li> <li>250 Hz sampling frequency</li> <li>Accelerometer</li> <li>Taped to lower back (L4)</li> <li>White, M.G.E.; Bezodis, N.E.; Neville, J.; Summers, H.; Rees, P. Determining jumping performance from a single body-worn accelerometer using machine learning. PLOS ONE 2022, 17, e0263846. <a href="https://doi.org/10.1371/journal.pone.0263846">https://doi.org/10.1371/journal.pone.0263846</a></li> </ul> </li> </ul> <p>MATLAB .mat files</p> <p>This repository was used by the paper currently under review for the open journal Mathematics:</p> <p>White, M.; De Lazzari, B.; Bezodis, N., Camomilla, V. Title. Mathematics 2024, 1, 0. Wearable Sensors for Athletic Performance: A Comparison of Discrete and Continuous Feature Extraction Methods for Prediction Models</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>
GEDII Wearable Sensors Dataset of 10 Research Teams
<p>The dataset contains Bluetooth (proximity), Infrared (face-to-face), Speech (microphone) and Accelerometer (body activity) data of 10 research teams collected during 5 working days in each team. Altogether N=105 team members. Socio-demographic data as well as round-robin ratings regarding friendship and advice seeking is included. Data was collected using Sociometric badges by Humanyze (formerly Sociometric Solutions).</p> <p>The present dataset has been produced within the context of a EU funded H2020 research project called “Gender-Diversity-Impact: Improving Research and Innovation through Gender Diversity. (GEDII)”. The project has been running from 2015 to 2018 with the aim to develop new tools and methods for doing research on the impact of gender diversity in R&D teams. In order to address these questions, GEDII makes use of a variety of research methods, including a cross country survey, bibliometric & patent analysis and detailed case studies with R&D teams.</p> <p>The dataset is distributed as R package.</p>
A database of physical therapy exercises with variability of execution collected by wearable sensors
<p>The PHYTMO database contains data from physical therapy exercises and gait variations recorded with magneto-inertial sensors, including information from an optical reference system. PHYTMO includes the recording of 30 volunteers, aged between 20 and 70 years old. A total amount of 6 exercises and 3 gait variations commonly prescribed in physical therapies were recorded. The volunteers performed two series with a minimum of 8 repetitions in each one. Four magneto-inertial sensors were placed on the lower-or upper-limbs for the recording of the motions together with passive optical reflectors. The files include the specifications of the inertial sensors and the cameras. The database includes magneto-inertial data (linear acceleration, turn rate and magnetic field), together with a highly accurate location and orientation in the 3D space provided by the optical system (errors are lower than 1mm). The database files were stored in CSV format to ensure usability with common data processing software. The main aim of this dataset is the availability of inertial data for two main purposes: the analysis of different techniques for the identification and evaluation of exercises monitored with inertial wearable sensors and the validation of inertial sensor-based algorithms for human motion monitoring that obtains segments orientation in the 3D space. Furthermore, the database stores enough data to train and evaluate Machine Learning-based algorithms. The age range of the participants can be useful for establishing age-based metrics for the exercises evaluation or the study of differences in motions between different aged groups. Finally, the MATLAB function <em>features_extraction</em>, developed by the authors, is also given. This function splits signals using a sliding window, returning its segments, and extract signal features, in the time and frequency domains, based on prior studies of the literature.</p>
Time Series data from wearable sensors to capture the onset of Fatigue in Runners
<p>The data captured came from mounting a single Shimmer3 IMU on the lumbar of 19 recreational runners. The participants were all regular runners and injury free. The study protocol was reviewed and approved by the human research ethics committee at University College Dublin.<br><br>The data was collected in three segments; in the first, the participant completed a 400m run at a comfortable pace; the second segment consisted of a beep test which acted as the fatiguing protocol for this study; and the last segment where the runner was required to complete the 400m run at their comfortable pace, this time in their fatigued state. The beep test requires the runner to continuously run between two points 20m apart following an audio which produces `beeps' indicating when the person should begin running from one end to the other. The test eventually requires the runner to increase their pace as the interval between the `beeps' reduces as the test progresses. The fatiguing protocol ends when the runner is unable to keep up the increase in pace. The runs were all done on an outdoor running track. The sensor captured acceleration, angular velocity and magnetometer data throughout the three stages of the trials at a sampling rate of 256Hz. The data included here consists of the raw readings from the sensors across the three phases of the run. The data is saved seperately as 'F' for Fatigued, 'NF' for Not Fatigued, and 'BeepTest' for the data collected during the fatiguing process.</p> <p>For the processed and labelled fatigue and non fatigue data, see:</p> <p>https://zenodo.org/records/7997851</p> <p>Kindly cite one of the following papers when using this data:</p> <p>B. Kathirgamanathan, B. Caulfield and P. Cunningham, "Towards Globalised Models for Exercise Classification using Inertial Measurement Units," 2023 IEEE 19th International Conference on Body Sensor Networks (BSN), Boston, MA, USA, 2023, pp. 1–4, doi: 10.1109/BSN58485.2023.10331612</p> <p>B. Kathirgamanathan, T. Nguyen, G. Ifrim, B. Caulfield, P. Cunningham. Explaining Fatigue in Runners using Time Series Analysis on Wearable Sensor Data, XKDD 2023: 5th International Workshop on eXplainable Knowledge Discovery in Data Mining, ECML PKDD, 2023, <a href="http://xkdd2023.isti.cnr.it/papers/223.pdf">http://xkdd2023.isti.cnr.it/papers/223.pdf</a></p>
WSD4FEDSRM (Wearable sensor data for fatigue estimation during shoulder rotation movements)
<p>The dataset comprises a collection of many data types during shoulder internal rotation, and external rotation exercises from 34 participants, including demographic information, anthropometric measurements, maximum voluntary isometric contraction force measurements, inertial measuring unit data, surface electromyography recordings, photoplethysmogram data from wearable sensors, as well as measurements from the Borg rating of perceived exertion scale and the Karolinska sleepiness scale.</p>
Data for paper titled : Comparing Clothing-Mounted Sensors with Wearable Sensors for Movement Analysis and Activity Classification (published in Sensors (MDPI))
<p>Data for paper titled : Comparing Clothing-Mounted Sensors with Wearable Sensors for Movement Analysis and Activity Classification (published in Sensors (MDPI))</p>
Dataset for Monitoring and Visualizing Stroke Rehabilitation Progress using Wearable Sensors (IMU)
<div> <p>This dataset is associated with a manuscript that is currently under peer review.</p> <p> </p> <p>Article Abstract:</p> <p>Stroke is one of the leading causes of death and disability worldwide, and recovering mobility is an important goal during post-stroke rehabilitation. In this work, we present a study to verify the feasibility of monitoring and visualizing longitudinal stroke gait rehabilitation progress using wearable sensors. Wearable devices such as inertial measurement units (IMUs) are easy-to-use and cost-effective tools for quantifying mobility. However, there is a need for research on longitudinal monitoring of stroke rehabilitation progress with wearables, as well as generating clinically relevant insights using appropriate visualizations. To this aim, we recruited ten stroke patients in their early rehabilitation stage. We collected and analyzed the IMU-derived gait features across two visits, and presented visualizations of the foot movement trajectories as well as the spatio-temporal gait parameters in the average, symmetry, and variation domains to quantify changes in gait. Our visualization and quantification methods are evaluated and validated by clinical experts, and prove to be promising in aiding clinicians to monitor rehabilitation progression.</p> <p> </p> <p>Data description:</p> <p>The dataset consists data from ten stroke patients who completed both visits. The "raw" data folder contains tri-axial acceleration and angular velocity data from the IMUs. In addition, information about the participants such as demographics (e.g., body height and body weight), FAC scores at both visits, and evaluations of gait improvement are documented in the file "participant_info.csv".</p> <p>The “interim” folder contains IMU data that has been manually segmented to remove irrelevant movements before and after each walking session during a visit, based on visual inspection of raw IMU signals. For quality control, the segmented accelerometer and gyroscope data of each sensor were plotted, and the plots were saved in the same folder as the IMU signals. In addition, during the first execution of gait parameter extraction, calculated 3D feet trajectories were cached in the "interim" folder, so that for future executions, the cached trajectories can be loaded directly, reducing the computational efforts for re-calculation. The file "stance_magnitude_thresholds_manual.csv" documents the angular velocity thresholds used to identify stance phases for the gait analysis algorithm for each participant. The threshold values were determined manually by observing the angular velocity signals. </p> <p>The “processed” folder contains stride-by-stride spatio-temporal gait parameters extracted for each of the four walking conditions, and aggregated gait parameters in terms of coefficients of variation and symmetry for all walking conditions for each participant. </p> <p> </p> </div>
Dataset for "Exposure and environmental engagement: A pilot integrating wearable sensors, air quality and citizen science"
<p>The dataset contains anonymised readings of 7 citizens taking air quality measurements using PlumeLabs Flow 2 monitor. Data is for Falmouth/Penryn, and Bristol and it was collected between January 26, 2022 and March 9, 2022.</p> <p>CSV file:</p> <ul> <li>latitude: unit degrees, positive values indicate North hemisphere.</li> <li>longitude, unit degrees, positive values indicate East.</li> <li>AQI: PlumeLabs' Air Quality Index.</li> <li>site: A refers to Falmouth/Penryn(UK), B refers to Bristol (UK).</li> <li>count: auxiliary variable that indicates that the record was comprised of a single reading.</li> </ul> <p>Jupyter notebook: The air quality analysis was conducted with Python 3.9.16 alongside numpy 1.24.3, pandas 2.0.2, matplotlib 3.7.1, and cartopy 0.21.1 (background tiles by OpenStreetMaps).</p>
Load position and weight classification during carrying gait using wearable inertial and electromyographic sensors
<p>This repository contains data from our study titled "Load position and weight classification during carrying gait using wearable inertial and electromyographic sensors." The following file types are included:</p> <p>- Basic participant demographics can be found in participants.xls.</p> <p>- README.pdf contains a detailed description of what can be found in each file.</p> <p>- SX_EMG.mat contains the EMG data for participant X. The file consists of EMG data for left and right erector spinae together with the time vector from that participant.</p> <p>- SX_Xsens.rar contains the Xsens data for participant X. This includes all joint angles and gait step time stamps from the sensors.</p> <p> </p>
Close encounters between infants and household members measured through wearable proximity sensors
<p>The dataset contains close proximity interactions between family members of 16 households with infants younger than 6 months, recorded for 2-5 consecutive days between March 2015 and January 2016, in Rome, Italy. Data were collected trough the use of wearable proximity sensors of the SocioPatterns platform (<a href="http://sociopatterns.org">http://sociopatterns.org</a>).</p> <p>Contact events were recorded between 55 individuals: 16 infants, 4 siblings, 31 parents and 4 grandparents.</p> <p>Each line of the dataset corresponds to a contact event recorded between two sensors (sensor 1 and sensor 2). Heading labels are the following:</p> <ul> <li>ID_sensor1: anonymized ID of sensor 1;</li> <li>ID_sensor2: anonymized ID of sensor 2;</li> <li>contact_duration: duration of the contact event in seconds;</li> <li>time: date and time of the contact event;</li> <li>family_role_tag1: family role of individual wearing sensor 1;</li> <li>Household: household ID;</li> <li>family_role_tag2: family role of individual wearing sensor 2.</li> </ul> <p> </p>
Dataset supporting publication: "Data collected by coupling fix and wearable sensors for addressing urban microclimate variability in an historical Italian city"
<p>Dataset supporting publication: “Data collected by coupling fix and wearable sensors for addressing urban microclimate variability in an historical Italian city” (publication available for download: <a href="https://zenodo.org/record/3901556">GEOFIT Zenodo</a>)</p> <p>Datasets resulting from monitoring activities of Sant'Apollinare systems and climatic parameters inside and outside the building (post-intervention monitoring).</p> <p>The article presents the data collected through an extensive research work conducted in a historic hilly town in central Italy during the period 2016-2017. Data concern two different datasets: long-term hygrothermal histories collected in two specific positions of the town object of the research, and three environmental transects collected following on foot the same designed path at three different time of the same day, i.e. during a heat wave event in summer. The short-term monitoring campaign is carried out by means of an innovative wearable weather station specifically developed by the authors and settled upon a bike helmet. Data provided within the short-term monitoring campaign are analysed by computing the apparent temperature, a direct indicator of human thermal comfort in the outdoors. All provided environmental data are geo-referenced. These data are used in order to examine the intra-urban microclimate variability. Outcomes from both long- and short-term monitoring campaigns allow to confirm the existing correlation between the urban forms and functionalities and the corresponding local microclimate conditions, also generated by anthropogenic actions. In detail, higher fractions of built surfaces are associated to generally higher temperatures as emerges by comparing the two long-term air temperature data series, i.e. temperature collected at point 1 is higher than temperature collated at point 2 for the 75% of the monitored period with an average of þ2.8 [1]C. Furthermore, gathered environmental transects demonstrate the high variability of the main environmental parameters below the Urban Canopy. Diversification of the urban thermal behaviour leads to a computed apparent temperature range in between 33.2 [1]C and 46.7 [1]C at 2 p.m. along the monitoring path. Reuse of these data may be helpful for further investigating interesting correlations among urban configuration, anthropogenic actions and microclimate variables affecting outdoor comfort. Additionally, the proposed dataset may be compared to other similar datasets collected in other urban contexts around the world. Finally, it can be compared to other monitoring methodologies such as weather stations and satellite measurements available in the location at the same time.</p>
CMT1A-BioStampNPoint2023: Charcot-Marie-Tooth disease type 1A accelerometry dataset from three wearable sensor study
<p>The CMT1A-BioStampNPoint2023 dataset provides data from a wearable sensor accelerometry study conducted for studying gait, balance, and activity in 15 individuals with Charcot-Marie-Tooth disease Type 1A (CMT1A). In addition to individuals with CMT1A, the dataset also includes data for 15 controls that also went through the same in-clinic study protocol as the CMT1A participants with a substantial fraction (9) of the controls also participating in the in-home study protocol. For the CMT1A participants, data is provided for 15 participants for the baseline visit and associated home recording duration and, additionally, for a subset of 12 of these participants data is also provided for a 12-month longitudinal visit and associated home recording duration. For controls, no longitudinal data is provided as none was recorded. The data were acquired using lightweight MC 10 BioStamp NPoint sensors (MC 10 Inc, Lexington, MA), three of which were attached to each participant for gathering data over a roughly one day interval. For additional details, see the description in the "README.md" included with the dataset.</p>
Assessing the Maturity level of Wearable Sensors for Home Monitoring in Parkinson's Disease through Evidence Evaluation Levels (EEL) and User Experience: A Comprehensive Review
<p>Source files for the PRISMA diagram and Figure 1 of the comprehensive review: Assessing the Maturity level of Wearable Sensors for Home Monitoring in Parkinson’s Disease through Evidence Evaluation Levels (EEL) and User Experience</p>
The ADAPT-study: Measuring Physical Performance Using Wearable Sensors in Parkinson's Disease and COPD (ADAPT)
ClinicalTrials.gov study NCT05756075. IPD Sharing: YES. Countries: 1. Publications: 1.
Wearable Sensors in Knee OA
ClinicalTrials.gov study NCT04243096. IPD Sharing: YES. Countries: 1. Publications: 2.
UV Exposure Assessed With Wearable Sensor and Sun Protection
ClinicalTrials.gov study NCT03344796. IPD Sharing: NO. Countries: 1. Publications: 3.
Can Early Initiation of Rehabilitation With Wearable Sensor Technology Improve Outcomes in mTBI?
ClinicalTrials.gov study NCT03479541. IPD Sharing: NO. Countries: 1. Publications: 6.
Monitoring Health Care Workers at Risk for COVID-19 Using Wearable Sensors and Smartphone Technology
ClinicalTrials.gov study NCT04756869. IPD Sharing: YES. Countries: 1. Publications: 1.
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.