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23 results for “mobile sensors”
Continuous monitoring of patient mobility for 18 months using inertial sensors following traumatic knee injury: a case study
<p><strong>This repository contains raw data relating to: </strong>Continuous monitoring of patient mobility for 18 months using inertial sensors following traumatic knee injury: a case study Mueller A., Hoefling H., Nuritdinow T., et al. DOI: 10.1159/000490919</p> <p><strong>Metadata and processed data derived from the raw data deposited here is available here:</strong> https://github.com/Novartis/mueller_et_al_2018</p> <p><strong>Article Abstract</strong></p> <p>Continuous patient activity monitoring during rehabilitation, enabled by digital technologies, will allow the objective capture of real-world mobility and aligning treatment to each individual’s recovery trajectory in real time. To explore the feasibility and added value of such approaches, we present a case study of a 36-year-old male participant monitored continuously for activity levels and gait parameters using a waist-worn inertial sensor following a tibial plateau fracture on the right side, sustained as a result of a high-energy trauma during a sporting accident. During rehabilitation, data were collected for a period of 553 days, with > 80% daytime compliance, until the participant returned to near full mobility. The participant completed a daily diary with the annotation of major events (falls, near falls, cycling periods, or physiotherapy sessions) and key dates in the patient’s recovery, including medical interventions, transitioning off crutches, and returning to work. We demonstrate the feasibility of collecting, storing, and mining of continuous digital mobility data and show that such data can detect changes in mobility and provide insights into long-term rehabilitation. We make both raw data and annotations available as a resource with the aspiration that further methods and insights will be built on this initial exploration of added value and continue to demonstrate that continuous monitoring can be deployed to aid rehabilitation.</p>
User stories and xAPI statements for "A mobile campus application as a sensor node for Personal Learning Environments"
<p>This dataset provides the full user stories and xAPI statements as used in the prototype described in the article "A mobile campus application as a sensor node for Personal Learning Environments". It consists of two PDF documents described below. The files were created as part of the master thesis of Hendrik Geßner.</p> <p>"User Stories.pdf" contains a complete list of user stories with required context information, existing portlets and a category. The process that led to this collection is described very briefly in the article mentioned above, a graphical explanation is available in the attached image "Use case process complete.jpg"</p> <p>"xAPI Statements.pdf" contains all xAPI statements used in the prototype described in the article mentioned above. Dynamic elements such as names or IDs are highlighted on color. The statements appear in the following order: attended, used, loggedin, wasat, opened, closed, joined, left.</p>
AirHeritage Datalake: Multi-site, Multi-season, Multi Unit dataset including Fixed and Mobile Citizen science data from networked Air Quality Low-Cost Multi-Sensors devices and reference stations
<p>This datalake comprises several datasets from <strong>37 networked low cost air quality multisensors</strong> (<strong>30</strong> <strong>mobile</strong> ENEA MONICA(tm) + <strong>7</strong> <strong>fixed</strong>) along with <strong>3</strong> (fixed) + <strong>1</strong> (mobile) <strong>reference stations</strong> operated by Campania Regional Envronmental Protection Agency. The datalake is organized in 3 main directories respectively related to fixed nodes, mobile nodes and nearby reference stations including a mobile laboratory used for colocation campaigns; each subdirectory include its own metadata description file.</p> <p>Data, curated by Energy and Data Science Laboratory of ENEA, include multi-weeks colocation periods when low cost devices have been colocated with reference stations as well as operational periods during which sensors are deployed for fixed or mobile monitoring campaigns. Data have been recorded during 2021 and 2022 in a<strong> pervasive, multi-site, multi-seasonal deployment</strong> in Portici, a densely populated small area city (4km2, 55k + inhabitants) located 7km south of Naples, Italy.</p> <p>The datalake consists in actual sensors and reference intrumentations timeseries along with metadata description files with deployment dates and location data. The dataset files include high sampling frequency raw sensor data of quality-controlled sensor network along with co-located reference stations data sets. Sensor data include electrochemical sensors data (intended target pollutants: NO2, O3, CO), Optical sensor data (PM2.5, PM10, PM1) readings along with meteorological parameters. .</p> <p>Further description of sensors and reference instruments are reported in the accompanying paper (see citation request).</p> <p>The dataset can be used for </p> <ul> <li> <strong>advanced (remote/universal/in field) data driven calibration strategies</strong> test or development including <strong>machine learning </strong>models</li> <li><strong>mobile opportunistic data fusion</strong> methods development</li> <li><strong>geomatics and data assimilation</strong> models studies</li> </ul> <p>as well as low cost sensor characterization performance studies. </p>
Dataset of "Social Robots and Sensors for Enhanced Ageing at Home: A Focus on Mobility and Socioeconomic Factors."
<p>This dataset supports the article:</p> <p>"Social Robots and Sensors for Enhanced Aging at Home: A Focus on Mobility and Socioeconomic Factors."</p> <p>For further details see the Readme.txt file.</p>
Ozone and Carbon Monoxide Dataset Collected by the OpenSense Zurich Mobile Sensor Network
<p><strong>Ozone and Carbon Monoxide Dataset Collected by the OpenSense Zurich Mobile Sensor Network</strong></p> <p>This dataset contains ozone (O3) and carbon monoxide (CO) concentration measurements collected by the OpenSense (<a href="http://www.opensense.ethz.ch">http://www.opensense.ethz.ch</a>) mobile senor network over the course of 4.5 years (2012/02-2016/09). The sensors are mounted on top of 10 streetcars in the city of Zurich, Switzerland.</p> <p><br> In particular, the dataset contains: </p> <ol> <li>Ozone (O3) data: 2012/02 - 2016/09 (19.9 Mio samples)</li> <li>Carbonmonoxide (CO) data: 2014/03 - 2016/09 (49.7Mio samples)</li> </ol> <p><strong>Hardware:</strong><br> --------------</p> <ol> <li>Ozone sensor: SGX (former e2V) MiCS-OZ-47 Ozone Sensing Head with Smart Transmitter PCB</li> <li>Carbon monoxide sensor: Alphasense CO-B4</li> <li>GPS receiver: u-blox EVK-6p</li> </ol> <p><strong>Data files format: </strong><br> -------------------------<br> co_data_*: </p> <ol> <li>Time of day: yyyy.mm.dd HH:MM</li> <li>Latitude WGS84</li> <li>Longitude WGS84</li> <li>HDOP: horizontal dilution of precision, uncertainty of the GPS position</li> <li>Tram ID</li> <li>WE_CHANNEL_SENSOR_1_MV: The voltage [in mV] at the working electrode of the electrochemical sensor (see Alphasense CO-B4 datasheet for more details)</li> </ol> <p>o3_data_*: </p> <ol> <li>Time of day: yyyy.mm.dd HH:MM</li> <li>Latitude WGS84</li> <li>Longitude WGS84</li> <li>HDOP: horizontal dilution of precision, uncertainty of the GPS position</li> <li>Tram ID</li> <li>Ozone [ppb]: On-device calibrated (according to manufacturer) ozone measurement [in parts-per-billion] </li> <li>Temperature [in °C]</li> <li>Relative Humidity [in %]</li> </ol> <p><strong>Data quality:</strong><br> ------------------<br> The data has NOT been post-processed!<br> In order to achieve high data quality, the data needs to be cleaned (e.g. outlier filtering) and, most importantly, the sensors need to be individually calibrated.<br> Reference data can be obtained from <a href="http://www.ostluft.ch">www.ostluft.ch</a>, the official air quality monitoring network in eastern Switzerland, which operates multiple monitoring stations in the city of Zurich.</p> <p><strong>Plot Coverage Map (MATLAB):</strong><br> --------------------------------------------<br> The provided MATLAB script plot_data_coverage.m plots the locations of the collected samples onto the map of Zurich (map_zurich.png). </p> <p><strong>References:</strong><br> -----------------<br> The dataset (and related aspects) has partly been used and is described in more detail in the following publications:</p> <ol> <li>Balz Maag et al. <strong>SCAN: Multi-Hop Calibration for Mobile Sensor Arrays</strong>. In Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, Vol.1, No.2 (IMWUT), 2017.</li> <li>Olga Saukh et al. <strong>Reducing Multi-Hop Calibration Errors in Mobile Sensor Networks</strong>. In IEEE/ACM International Conference on Information Processing in Sensor Networks (IPSN), 2015. Best Paper Award!</li> <li>Olga Saukh et al. <strong>Route Selection for Mobile Sensor Nodes on Public Transport Networks</strong>. In Journal of Ambient Intelligence and Humanized Computing, 5(3), Springer, 2014.</li> <li>Olga Saukh et al. <strong>On Rendezvous in Mobile Sensing Networks</strong>. In Proceedings of the 5th Workshop on Real-World Wireless Sensor Networks (RealWSN), 2013.</li> <li>Jason Jingshi Li et al. <strong>Sensing the Air we Breathe – The OpenSense Zurich Dataset</strong>. In Proceedings of the 26th International Conference on Artificial Intelligence (AAAI), 2012.</li> <li>Olga Saukh et al. <strong>Route Selection for Mobile Sensors with Checkpointing Constraints</strong>. In Proceedings of the 8th International Workshop on Sensor Networks and Systems for Pervasive Computing (PerSeNS, in conjunction with IEEE PerCom), March 2012.</li> <li>David Hasenfratz et al. <strong>On-the-fly Calibration of Low-Cost Gas Sensors</strong>. In Proceedings of the 9th European Conference on Wireless Sensor Networks (EWSN), 2012. </li> </ol> <p><br> For further information, visit: <a href="http://www.opensense.ethz.ch">http://www.opensense.ethz.ch</a></p>
Raw Sensor Data for STRIDE Project J "Improving Work Zone Mobility through Planning, Design and Operations"
<p>This dataset contains the raw traffic data from 9 sensors located on I-59 southbound near Tuscaloosa, Alabama, from October 3 to October 16, 2016. These data were used in the research for STRIDE Project J "Improving Work Zone Mobility through Planning, Design and Operations" and described in the STRIDE Final Project for this project.</p>
Mobile Sensor Readings of Activities
<p>This collection contains mobile sensor readings used to train an activity recognition classifier. The sensor data was accessed via the Generic Sensor API. Mobile sensor readings from the following sensors:</p> <ul> <li>accelerometer</li> <li>gyroscope</li> <li>magnetometer</li> <li>gravity sensor</li> <li>relative orientation sensor</li> <li>absolute orientation sensor</li> <li>linear acceleration sensor</li> </ul> <p>Recording were measured while the following activities were measured:</p> <ul> <li>Lying</li> <li>Sitting</li> <li>Standing</li> <li>Walking</li> <li>Stairs up</li> <li>Stairs down</li> <li>Running</li> <li>Taking the car</li> <li>Taking the bus</li> <li>Taking the tram</li> <li>Taking the train</li> <li>Taking the metro</li> </ul> <h3>Structure of the dataset</h3> <p>The collected data in each recording includes the start time timestamp readingID, the activity label activity, the duration<br>of the recording in elapsedTime, and an object sensorData, which contains the lengthy sensor measurements. The measurements for each sensor is stored in the form of an array containing on each index a series of four values. The time of when the measurement was taken t, and the value in the three axes x,y and z.</p> <p>A minimalistic example of the format of the collected data:</p> <p>{<br>"readingID": 1710151914418,<br>"activity": ""walk"",<br>"elapsedTime": 17312<br>"sensorData": {<br>"acce": [<br>...<br>{<br>"t": 1624,<br>"x": 0.05,<br>"y": -0.61,<br>"z": 0.21,<br>},<br>... ]<br>"gyro": Array (844)<br>"magnet": Array (136)<br>}<br>}</p> <p> </p> <p>A big thanks to all the volunteers who helped collect this dataset.</p> <p> </p>
Ultrafine Particle Dataset Collected by the OpenSense Zurich Mobile Sensor Network
<p><strong>Ultrafine Particle Dataset Collected by the OpenSense Zurich Mobile Sensor Network</strong></p> <p>This dataset contains over 2 and a half years (04/2012-12/2014, >36 Mio samples) worth of ultra-fine particle (UFP) concentration measurements collected by a mobile senor network. The sensors are mounted on top of 10 streetcars in the city of Zurich, Switzerland.</p> <p><strong>Hardware:</strong></p> <ul> <li><strong>Ultrafine particle sensor</strong>: MiniDiSC (see also: Martin Fierz et al. Design, Calibration, and Field Performance of a Miniature Diffusion Size Classifier. Aerosol Science and Technology, Volume 45, 2011.)</li> <li><strong>GPS receiver</strong>: u-blox EVK-6p<br> </li> </ul> <p><strong>Sensor Data<br> ------------------</strong><br> <strong>ufp_data</strong><strong>*.csv column format:</strong></p> <ol> <li>Time of day: yyyy.mm.dd HH:MM</li> <li>Latitude WGS84</li> <li>Longitude WGS84</li> <li>HDOP: horizontal dilution of precision, uncertainty of the GPS position</li> <li>Tram ID</li> <li>Number of particles [#/ccm]</li> <li>Average particle diameter [nm]</li> <li>LDSA: lung deposited surface area [um2 /cm3]</li> </ol> <p><strong>Data quality:</strong><br> The data has been post-processed by performing a periodic null-offset calibration and filtering samples during malfunction.</p> <p><strong>High-Resolution Maps<br> --------------------------------</strong></p> <p>The data has been used to create high-resolution ultrafine particle concentration maps. Four maps, which show the seasonal average particle concentration over seasonal periods, can be found in ufp_seasonal_maps_201204_201304.csv.</p> <p><strong>ufp_map*.csv column format:</strong></p> <ol> <li>Latitude WGS84</li> <li>Longitude WGS84</li> <li>Estimated number of particles [#/ccm]</li> </ol> <p><strong>Map quality</strong></p> <p>Please have a look at the papers in References 1. and 2. (Hasenfratz et al. 2014 and 2015) for a detailed evaluation of the maps.</p> <p><strong>References<br> ----------------</strong><br> The dataset has been used and is described in more detail in the following publications:</p> <ol> <li>David Hasenfratz et al.<em> Pushing the Spatio-Temporal Resolution Limit of Urban Air Pollution Maps.</em> IEEE International Conference on Pervasive Computing and Communications (PerCom). Budapest, Hungary, March 2014. Best Paper Award. </li> <li>David Hasenfratz et al. <em>Deriving High-Resolution Urban Air Pollution Maps Using Mobile Sensor Nodes. </em>Pervasive and Mobile Computing. Elsevier, 2015. </li> <li>David Hasenfratz et al. <em>Demo Abstract: Health-Optimal Routing in Urban Areas.</em> ACM/IEEE International Conference on Information Processing in Sensor Networks (IPSN). Seattle, USA, April 2015.</li> <li>Michael Müller et al. <em>Statistical modelling of particle number concentration in Zurich at high spatio-temporal resolution utilizing data from a mobile sensor network. </em>Atmospheric Environment. Elsevier, 2016.</li> </ol> <p>For further information, visit: <a href="http://www.opensense.ethz.ch">http://www.opensense.ethz.ch</a></p>
Sense2Stop:Mobile Sensor Data to Knowledge
ClinicalTrials.gov study NCT03184389. IPD Sharing: YES. Countries: 1. Publications: 4.
Distributed Sensing with Low-cost Mobile Sensors towards a Sustainable IoT
<p>This zip file contains the dataset used to produce Figure 4 of "Distributed Sensing with Low-cost Mobile Sensors towards a Sustainable IoT".</p> <p>It contains two folders, the first for the stationary sensors and the second for the mobile sensors.</p>
Artificial Intelligence Mobile Health Trial Of A Digital Platform To Optimize GDMT Using Wearable Sensors
ClinicalTrials.gov study NCT04191330. IPD Sharing: NO. Countries: 1. Publications: 1.
Personalized Mobility Interventions Using Smart Sensor Resources for Lower-Limb Prosthesis Users
ClinicalTrials.gov study NCT03930199. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Personalized Depression Treatment Supported by Mobile Sensor Analytics
ClinicalTrials.gov study NCT06292221. IPD Sharing: NO. Countries: 1. Publications: 1.
Use of a Mobile Health Sensor in an Open Label Trial of Lorcaserin for the Treatment of Cannabis Use Disorder
ClinicalTrials.gov study NCT02932215. IPD Sharing: UNDECIDED. Countries: 1. Publications: 1.
Data from: Using mobile phones as acoustic sensors for high-throughput mosquito surveillance
Open the record for dataset details and reuse information.
Monitoring of Weight Bearing Protocols Via Mobile Application and Pressure Sensors.
ClinicalTrials.gov study NCT04194970. IPD Sharing: NO. Countries: 1. Publications: 0.
Accuracy of a Mobile Sensor-Based System for the Detection of Chemotherapy Toxicity in Older Adults With Cancer in a Developing Country
ClinicalTrials.gov study NCT04040881. IPD Sharing: NO. Countries: 1. Publications: 0.
G5® Mobile Continuous Glucose Monitoring System Automated vs. Manual Sensor Applicator
ClinicalTrials.gov study NCT02696252. IPD Sharing: UNDECIDED. Countries: 1. Publications: 0.
A Clinimetric Test of Spinal Sensors in Measuring Spinal Mobility in Axial Spondyloarthritis
ClinicalTrials.gov study NCT03159767. IPD Sharing: NO. Countries: 1. Publications: 0.
Accessing Mobility Using Wearable Sensors
ClinicalTrials.gov study NCT04306588. IPD Sharing: Not stated. Countries: 1. Publications: 0.
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.