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659 results for “wearables”
Self-Annotated Wearable Activity Data
<p>Our dataset contains 2 weeks of approx. 8-9 hours of acceleration data per day from 11 participants wearing a <a href="https://shop.espruino.com/banglejs">Bangle.js Version 1</a> smartwatch with our <a href="https://github.com/kristofvl/BangleApps/tree/master/apps/activate_test">firmware</a> installed.</p> <p>The dataset contains annotations from 4 different commonly used annotation methods utilized in user studies that focus on in-the-wild data. These methods can be grouped in user-driven, in situ annotations - which are performed before or during the activity is recorded - and recall methods - where participants annotate their data in hindsight at the end of the day.</p> <p>The participants had the task to label their activities using (1) a button located on the smartwatch, (2) the activity tracking app <a href="https://www.strava.com/">Strava</a>, (3) a (hand)written diary and (4) a tool to visually inspect and label activity data, called <a href="https://github.com/mad-lab-fau/mad-gui">MAD-GUI</a>. Methods (1)-(3) are used in both weeks, however method (4) is introduced in the beginning of the second study week.</p> <p>The accelerometer data is recorded with 25 Hz, a sensitivity of ±8g and is stored in a csv format. Labels and raw data are not yet combined. You can either write your own script to label the data or follow the instructions in our corresponding <a href="https://github.com/ahoelzemann/annotationMatters">Github repository.</a></p> <p>The following unique classes are included in our dataset:</p> <p>laying, sitting, walking, running, cycling, bus_driving, car_driving, vacuum_cleaning, laundry, cooking, eating, shopping, showering, yoga, sport, playing_games, desk_work, guitar_playing, gardening, table_tennis, badminton, horse_riding.</p> <p>However, many activities are very participant specific and therefore only performed by one of the participants.</p> <p>The labels are also stored as a .csv file and have the following columns:</p> <p><strong>week_day, start, stop, activity, layer</strong></p> <p>Example:</p> <p>week2_day2,10:30:00,11:00:00,vacuum_cleaning,d</p> <p>The <em>layer</em> columns specifies which annotation method was used to set this label.</p> <p>The following identifiers can be found in the column:</p> <p>b: in situ <strong>button</strong></p> <p>a: in situ <strong>app</strong></p> <p>d: self-recall <strong>diary</strong></p> <p>g: time-series recall labelled with a the <strong>MAD-GUI</strong></p> <p> </p> <p>The corresponding publication is currently under review.</p>
Wearable Device Dataset from Induced Stress and Structured Exercise Sessions
<p>This original dataset contains physiological signals collected during structured acute stress induction and aerobic and anaerobic exercise sessions using a wearable device. Blood volume pulse, motion-based activity, skin temperature, and electrodermal activity were recorded with the Empatica E4, a research-grade wearable. The stress induction protocol involved math and emotional tasks designed to provoke stress responses, interleaved with rest periods. Self-reported stress levels were also recorded during this procedure. For the exercise sessions, distinct routines on a stationary bike were created for aerobic and anaerobic activities. The dataset includes records from 36 healthy volunteers for stress sessions, 30 for aerobic exercise, and 31 for anaerobic exercise. By examining the variations in physiological signals, the effects of these activities can be analyzed. This dataset is a valuable resource for research on stress and exercise detection and classification.</p>
Daisy anonymized wearable raw data
<p>This is the raw wearable dataset from the paper by the authors, titled "Feasibility and patient acceptability of a commercially available wearable and a smartphone application in identification of motor states in Parkinson<strong>’</strong>s disease", PLOS Digital Health 2023, DOI 0.1371/journal.pdig.0000225</p> <p>The HDF5 file has groups as subjects, P_* denoting patients and C_* denoting controls. </p> <p>Within each group, index "1" denotes accelerometer data, other indices are heart rate and other data not used in the study. For example, /P_fd3e/1/timestamp is the timestamp of the accelerometer data of (anonymized) patient P_fd3e, and /P_fd3e/1/y are their corresponding values measured from the y-channel of the device. </p> <p> </p> <p> </p>
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>
Mechanical Shaker Experiments: Amsterdam Study into the Properties of Wearable Accelerometers (ASPWA)
<p><strong>Mechanical Shaker Experiments: Amsterdam Study into the Properties of Wearable Accelerometers (ASPWA)</strong></p> <p>A description of the data files in this archive can be found in: 20230816_Documentation-raw-folder-structure.pdf</p> <p>If you have a question:</p> <ol> <li>use the search functionality <a href="https://github.com/wadpac/mechanicalshakerexperiments/issues">here</a> to see if someone already experienced the same issue;</li> <li>if your search did not yield any relevant results, please start a new conversation.</li> </ol>
In-Home Technology for Caregivers of People With Dementia and Mild Cognitive Impairment: Wearables
ClinicalTrials.gov study NCT05159557. IPD Sharing: YES. Countries: 1. Publications: 4.
A non-contact wearable device for monitoring epidermal molecular flux
Open the record for dataset details and reuse information.
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>
Comfort and wearability of orthodontic mouthguards during contact sports in adolescent patients undergoing fixed appliance orthodontic treatment: a randomised clincal trial
<p>Dataset for all analyses in the paper</p>
ScientISST MOVE: Annotated Wearable Multimodal Biosignals recorded during Everyday Life Activities in Naturalistic Environments
<p>A multi-modality, multi-activity, and multi-subject dataset of wearable biosignals.</p><p><strong>Modalities:</strong> ECG, EMG, EDA, PPG, ACC, TEMP</p><p><strong>Main Activities:</strong> Lift object, Greet people, Gesticulate while talking, Jumping, Walking, and Running</p><p><strong>Cohort: </strong>17 subjects (10 male, 7 female); median age: 24</p><p><strong>Devices: </strong>2x ScientISST Core + 1x Empatica E4</p><p><strong>Body Locations: </strong>Chest, Abdomen, Left bicep, wrist and index finger</p><p>No filter has been applied to the signals, but the correct transfer functions were applied, so the data is given in relevant unis (mV, uS, g, ºC).</p><p>For more information on background, methods and the acquisition protocol, refer to <a href="https://doi.org/10.13026/0ppk-ha30">https://doi.org/10.13026/0ppk-ha30</a>.</p><p>========</p><p>In this repository, there are two formats available:</p><h4><strong>a) LTBio's Biosignal files. Should be open like:</strong></h4><p><i>x = Biosignal.load(path)</i></p><p>LTBio Package: <a href="https://pypi.org/project/LongTermBiosignals/">https://pypi.org/project/LongTermBiosignals/</a></p><p>Under the directory <i>biosignal</i>, the following tree structure is found: <i>subject/x.biosignal</i>, where <i>subject</i> is the subject's code, and <i>x</i> is any of the following {<i> acc_chest, acc_wrist, ecg, eda, emg, ppg, temp</i> }. Each file includes the signals recorded from every sensor that acquires the modality after which the file is named, independently of the device.</p><p>Channels, activities and time intervals can be easily indexed with the index operator <i>[]</i> ( <a href="https://ltbio.readthedocs.io/en/latest/learn/basic/ltbio101.html">https://ltbio.readthedocs.io/en/latest/learn/basic/ltbio101.html</a> ).</p><p>A sneak peak of the signals can also be quickly plotted with: <i>x.preview.plot()</i></p><p>Any Biosignal can be easily converted to NumPy arrays or DataFrames, if needed.</p><h4><strong>b) CSV files. Can be open like:</strong></h4><p><i>x = pandas.read_csv(path)</i></p><p>Pandas Package: <a href="https://pypi.org/project/pandas/">https://pypi.org/project/pandas/</a></p><p>These files can be found under the directory <i>csv</i>, named as <i>subject.csv</i>, where <i>subject</i> is the subject's code. There is only one file per subject, containing their full session and all biosignal modalities. When read as tables, the time axis is in the first column, each sensor is in one of the middle columns, and the activity labels are in the last column. In each row are the samples of each sensor, if any, at each timestamp. At any given timestamp, if there is no sample for a sensor, it means the acquisition was interrupted for that sensor, which happens between activities, and sometimes for short periods during the running activity. Also in each row, on the last column, is one or more activity labels, if an activity was taking place at that timestamp. If there are multiple annotations, the labels are separated by vertical bars (e.g '<i>run | sprint</i>'). If there are no annotations, the column is empty for that timestamp.</p><p>In order to provide a tabular format with sensors with different sampling frequencies, the sensors with sampling frequency lower than 500 Hz were upsampled to 500 Hz. This way, the tables are regularly sampled, i.e., there is a row every 2 ms. If a sensor was not acquiring at a given timestamp, the corresponding cell with be empty. So, not only the segments with samples are regularly sampled, but the interruptions are also discretised. This means that if, after an interruption, a sensor starts acquiring at a non regular timestamp, the first sample will be written on the previous or the following timestamp, by half-up rounding. Naturally, this process cumulatively introduces lags in the table, some of which cancel out. Each individual lag is no longer than half the sampling period (1 ms), hence negligible. The cumulative lags are no longer than 48 ms for all subjects, which is also negligible. Nevertheless, only the LBio's Biosignal format preserves the exact original timestamps (10E-6 precision) of all samples and the original sampling frequencies.</p><p>================</p><p>Both include annotations of the activities, however LTBio bio signal files have better time resolution and include clinical data and demographic data as well.</p>
Seamless integration of conducting hydrogels in daily life: from preparation to wearable application
<p><span>The dataset contains the recent advances in the development of CHs for smart wearable devices. We summarize the synthesis of conducting polymers and the various approaches used to prepare CHs. We also analyze their </span><span>properties </span><span>and discuss the fabrication of specific geometries to improve the performance of the final CH-based wearable device</span><span>s.</span><span>. The studies presented herein contribute to the growing field of wearable devices by highlighting the potential of CHs as versatile and functional material platform</span><span>s. The development and integration of CHs present challenges and opportunities that highlight the need for novel fabrication techniques and advanced materials.</span></p>
Multi-task self-supervised learning for wearables - human activity recognition
<p>Datasets used to train and evaluated the self-supervised-learning model</p>
Wearable Network for Multi-Level Physical Fatigue Prediction in Manufacturing Workers - Dataset
<p>This dataset contains data from 43 subjects following two authentic manufacturing protocols and their self-reported fatigue score. The system employs 6 wearable sensors to continuously track vital and locomotive signs from multiple body locations.</p> <p> </p> <p>The goal of the experiment is to predict fatigue trends in a subject, while they are asked to perform pre-defined manual tasks simulating a manufacturing environment, using data from soft, flexible, wearable sensors and a vision system. The tasks in this study are repetitive and physically exerting involving intricate steps taken in real manufacturing settings. The iterative nature of the tasks facilitates comparative analyses of distinct temporal segments to characterize fatigue. The two manufacturing tasks are (1) Task Composite: Composite Sheet Layup, and (2) Task Harnessing: Wire Harnessing. The task protocol requires the subject to wear sensors to monitor vital and locomotive signs continuously. Additionally, we incorporate a weighted vest to exaggerate the induced fatigue in a reasonable duration for the study to mimic a full shift for a manufacturing worker. Each task consists of two rest periods of 5 minutes each at the start and end, as well as five segments of physical tasks. On average, each task takes a total of 1 hour. Before each data segment, the subject fills out a survey form to indicate their current fatigues as per the Borg scale. </p>
Raw Data for Energy Consumption Comparison of DMA-Based and FatFs Storage Systems on Wearable Devices
<p>This dataset contains raw oscilloscope measurements comparing the energy consumption of a Direct Memory Access (DMA)-based storage system versus the FatFs file system for wearable devices. The data was collected as part of the study "Direct Memory Access-Based Data Storage for Long-Term Acquisition Using Wearables in an Energy-Efficient Manner".</p> <p>The dataset includes voltage drop measurements across a 2-ohm shunt resistor, captured using an Analog Discovery 2 digital oscilloscope at a 500 kHz sampling rate. Measurements were taken under various conditions:</p> <ul> <li>Storage systems: DMA-based (proposed) and FatFs</li> <li>SD card capacities: 4 GB and 8 GB</li> <li>Write frequencies: 2 Hz and 5 Hz (referring to the frequency of writing a specific data block of 15,872 bytes)</li> <li>With and without a smoothing capacitor</li> </ul> <p>Each CSV file contains 20 million samples, equivalent to 40 seconds of data acquisition. File names encode the experimental conditions, including the storage system, write frequency, number of samples, acquisition rate, acquisition time, data format, SD card size, and absence of the smoothing capacitor.</p> <p>The data is organized into two main folders:</p> <ol> <li>"cap": Contains measurements with the smoothing capacitor</li> <li>"no_cap": Contains measurements without the smoothing capacitor</li> </ol> <p>This raw data can be used to reproduce the energy consumption and write speed analyses presented in the article, as well as for further investigation into the performance of embedded storage systems for wearable devices.</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>
Evaluation of tracking devices and elicitation of wearability requirements for animal-centred biotelemetry in cats
<p>Thirteen cat participants wearing GPS devices were monitored through ethologically-informed observations, designed specifically to measure the behaviour of the animals with the biotelemetry tags. Here, findings from the behavioural analysis are presented.</p>
Ground reaction force metrics are not strongly correlated with tibial bone load when running across speeds and slopes: implications for science, sport and wearable tech
<p>An interactive user interface and the raw data from the manuscript titled: "Ground reaction force metrics are not strongly correlated with tibial bone load when running across speeds and slopes: implications for science, sport and wearable tech". </p>
Dataset: Combining video telemetry and wearable MEG for naturalistic imaging
<p>OPM-MEG and Openpose keypoint data from the study "Combining video telemetry and wearable MEG for naturalistic imaging".</p> <p><strong>Changelog</strong></p> <p><strong>v1.10</strong></p> <ul> <li>Subject 004 from v1.01 has been renamed 005 (to reflect addition of new subject recorded prior to 005 during acquisition).</li> <li><strong>NEW </strong>sub-004</li> <li>Subjects 003-004 have a proof-of-principle motor paradigm added.</li> <li>README changes</li> </ul> <p><strong>v1.01</strong></p> <ul> <li>Corrected sub-004 *_channel.json files to include bad channel identifiers</li> <li>Telemetry data zipped prior to uploading to zenondo</li> <li>Updates to README</li> </ul>
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>
Research data supporting 'Fiber pumps for wearable fluidic systems'
<p>The dataset supporting 'Fiber pumps for wearable fluidic systems', published in <em>Science</em> in March 2023.</p> <p>In this work, we present a fluidic pump in the form of a flexible and stretchable tube. These tubes - or 'fiber pumps' - can generate significant fluidic pressure and flowrate without moving parts or vibration. They are intended to be combined with textiles and clothing. This allows high pressure fluidic circuits to be seamlessly integrated into wearable devices, powering soft supportive exosuits, thermoregulatory clothing, and wearable haptics. </p> <p>This dataset contains the characterisation data of the pumps themselves, as well as characterisation data of the demonstrations that incorporate these pumps. Pump characterisation data includes measurements of pressure, flowrate, power density and other pump metrics. Demo characterisation includes measurements of strain, force, thermal transport and other data. A detailed description of the measurements, file types and naming conventions can be found in the read me file included in the dataset. </p> <p> </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.