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28 results for “fall detection”
Fall Detection Dataset Modified - Unicomfacauca
<p>Modification of the original TST Fall detection dataset (available at <a href="https://meet.google.com/linkredirect?authuser=0&dest=https%3A%2F%2Fwww.ieee-dataport.org%2Fdocuments%2Ftst-fall-detection-dataset-v2">https://www.ieee-dataport.org/documents/tst-fall-detection-dataset-v2</a>) in order to more accurately represent the event of a fall, and also adds a class related to people standing.</p> <p>Source Code Repository: <a href="https://github.com/CristianChilito/Fall-Detection-System-Unicomfacauca">https://github.com/CristianChilito/Fall-Detection-System-Unicomfacauca</a></p>
Image dataset for the creation of an automatic system for meteor fall detection
<p>Image dataset with sky photos showing the occurrence or non-occurrence of falling meteors. The database comprises 7,000 images in JPEG format -- 3,850 (55%) images show the event of falling meteors, and 3,150 (45%) images show no meteors. Different instruments captured the photos from 2014 to 2023. We used the images to train a deep-learning neural network for an automatic falling meteor detector.</p> <p>The primary image data sources were the Brazilian Meteor Observation Network (BRAMON -- <a href="http://www.bramonmeteor.org">http://www.bramonmeteor.org</a>), UK Meteor Network (UKMON -- <a href="https://ukmeteornetwork.co.uk">https://ukmeteornetwork.co.uk</a>), and <em>Base des Observateurs Amateurs de Météores</em> (BOAM -- <a href="http://boam.fr">http://boam.fr</a>) repositories.</p> <p><strong>Folders Structure</strong></p> <p>We divided the folder structure into two levels. In the first level, we have two folders: RawImages, which holds images with captions stored in the repositories; and CroppedImages, which contains images without the captions (we cropped a band of 24 pixels in the lower part of the image).</p> <p>In the second level, in each of the previous folders, we have another two folders: meteor, which has images with meteors; and non-meteors, with images without occurrences of meteors.</p> <p><strong>Naming pattern for the files</strong></p> <p>The naming pattern in the meteor folder follows the format <source>_<date>_<id>.jpg where:</p> <ul> <li><source> is one of the 3 data sources: bramon, ukmon, or boam.</li> <li><date> is the date-time the instrument captured the image in the format yyyymmdd_hhnnss (y:year, m:month, d:day, h:hours, n:minutes, s:seconds).</li> <li><id> is an identifier from a specific source to avoid date-time conflicts: <ul> <li>BRAMON: radiant identifier.</li> <li>UKMON: station identifier.</li> <li>BOAM: station identifier.</li> </ul> </li> </ul> <p>For the non-meteor folder, the naming pattern is <source>_<date>_nonmeteor.jpg to avoid homonyms (with the same date-time) and to identify that they are images of non-meteors.</p>
ASSIST-IoT Multimodal Fall Detection Dataset
<p>Multimodal dataset for fall detection. Includes acceleration data collected from a tag and two smartwatches, and location reported by the tag. More details about the data collection procedure can be found in <code>notes.md</code>.</p> <p><strong>Contents</strong></p> <p>The repository contains:</p> <ul> <li><code>data/location_data.csv</code> and <code>data/full_acceleration</code> – preprocessed acceleration and location data from 10 participants and mannequin simulated falls with target variable identified</li> <li><code>data/subsampled_acceleration_data.csv</code> – subsampled acceleration dataset used for training the AI model</li> <li><code>notes.md</code> – description of activities performed and notes from data collection</li> <li><code>videos</code> – reference videos for performed activities</li> </ul> <p><strong>Authors</strong></p> <ul> <li><a href="https://orcid.org/0000-0002-2543-9461">Piotr Sowiński</a> – research methodology, data collection and processing</li> <li><a href="https://orcid.org/0000-0003-3217-1050">Monika Kobus</a> – research methodology, data collection</li> <li><a href="https://orcid.org/0000-0003-4295-3005">Anna Dąbrowska</a> – research methodology, methodological supervision</li> <li><a href="https://orcid.org/0000-0003-1524-7877">Kajetan Rachwał</a> – data collection</li> <li><a href="https://orcid.org/0000-0002-7109-891X">Karolina Bogacka</a> – research methodology</li> <li><a href="https://orcid.org/0000-0002-9572-2705">Krzysztof Baszczyński</a> – research methodology, data collection</li> <li><a href="https://orcid.org/0000-0002-3080-0303">Anastasiya Danilenka</a> – research methodology, data collection and processing</li> </ul> <p><strong>Acknowledgements</strong></p> <p>This work is part of the <a href="https://assist-iot.eu/">ASSIST-IoT project</a> that has received funding from the EU’s Horizon 2020 research and innovation programme under grant agreement No 957258.</p> <p>The <a href="https://www.ciop.pl/en">Central Institute for Labour Protection – National Research Institute</a> provided facilities and equipment for data collection.</p> <p><strong>License</strong></p> <p>The dataset is licensed under the <a href="https://creativecommons.org/licenses/by/4.0/">Creative Commons Attribution 4.0 International License</a>.</p>
An automated system for inspecting rock faces and detecting potential rock falls using machine learning
<p>Rockfall is a hazard in mountainous areas threatening infrastructure and human lives. Rockfall hazards are often mitigated by manual inspections using pry bars. The inspector must access the rock face, hit the rock surface, detect, and remove the loose rocks. This method is very labor demanding, unsafe, and challenging. This research presents a method that automatize the inspection of rock blocks that are prone to rockfall events. A robot is developed to replace the manual hammer tap process and collect the sound data remotely; subsequently, the sound signal is used to identify different types of the discontinuity in rocks in controlled laboratory environment. Machine learning is used to train the method to discriminate between intact rock and rock that may be prone to fall. This methodology was successfully applied to laboratory tests on rock. Finally, the research involves the implementation of this system in field to understand the potential and limitations of the proposing system in automatizing the rock inspections. This research enables the inspectors to collect data remotely, detect loose rocks, and save data for future references.</p>
Data from: Humans can use positive and negative spectrotemporal correlations to detect rising and falling pitch
Open the record for dataset details and reuse information.
ShimFall&ADL: Triaxial accelerometer fall and activities of daily living detection dataset
<p> </p> <p><strong>ShimFall&ADL dataset</strong></p> <p> </p> <p><strong>Version </strong>1.0 (2020-06-19)</p> <p><strong>Please cite as:</strong> "T. Althobaiti, S. Katsigiannis, N. Ramzan, Triaxial accelerometer-based Fall and Activities of Daily Life detection using machine learning, Sensors, 20(13), 3777, 2020. doi: 10.3390/s20133777"</p> <p> </p> <p><strong>Disclaimer</strong><br> While every care has been taken to ensure the accuracy of the data included in the ShimFall&ADL dataset, the authors and the University of the West of Scotland do not provide any guaranties and disclaim all responsibility and all liability (including without limitation, liability in negligence) for all expenses, losses, damages (including indirect or consequential damage) and costs which you might incur as a result of the provided data being inaccurate or incomplete in any way and for any reason. 2020, University of the West of Scotland, Scotland, United Kingdom.</p> <p><br> <strong>Contact</strong><br> For inquiries regarding the ShimFall&ADL dataset, please contact:<br> Dr Stamos Katsigiannis, Stamos.Katsigiannis@uws.ac.uk, University of the West of Scotland<br> Prof. Naeem Ramzan, Naeem.Ramzan@uws.ac.uk, University of the West of Scotland</p> <p> </p> <p><strong>Acknowledgment</strong></p> <p>The authors would like to thank Md. Hasan Shahriar for the data collection under his MSc project.</p> <p> </p> <p><strong>Dataset summary</strong><br> The ShimFall&ADL dataset contains recordings from 35 individuals, acquired using a chest-strapped Shimmer v2 tri-axial accelerometer, recording at a 50Hz sampling rate. Experiments were conducted in a controlled environment at a research lab in the University of the West of Scotland. Thirty five (35) healthy individuals were recruited among young or mid-aged volunteers, aged between 19 and 34 years old, having a body weight between 52 and 113 kg, and a body height between 1.45 and 1.82 m.</p> <p>Participants performed the following activities of daily living (ADL):<br> Jumping<br> Lying down<br> Bending/picking up<br> Sitting to a chair<br> Standing up from a chair<br> Walking</p> <p>Participants performed the following falls:<br> Steep (hard)<br> Front (soft)<br> Front (hard)<br> Left (soft)<br> Left (hard)<br> Right (soft)<br> Right (hard)<br> Back (soft)<br> Back (hard)</p> <p><br> <strong>Data</strong><br> Each ".dat" file in the dataset corresponds to one event for one individual and contains 101 accelerometer samples corresponding to the event. Each row of the file corresponds to one 3-channel sample, dividing the x, y, z axes values using the "\t" character, as follows:<br> Row 1: x1\ty1\tz1<br> Row 2: x2\ty2\tz2<br> ...<br> Row N: xN\tyN\tzN</p> <p>The files within the dataset are named as follows:<br> adl_<ADL activity>_<Participant ID>.dat<br> <Fall Type>fall_<soft,hard>_<Participant ID>.dat</p> <p>For example, the file "adl_standingfromchair_18.dat" corresponds to the accelerometer recording of the 18th participant, performing the "standing up from chair" ADL. The file, "leftfall_soft_11.dat" corresponds to the accelerometer recording of the 11th participant, performing a soft left fall.</p> <p><br> <strong>Additional information</strong><br> For additional information regarding the creation of the ShimFall&ADL dataset, please refer to the associated publication: "T. Althobaiti, S. Katsigiannis, N. Ramzan, Triaxial accelerometer-based Fall and Activities of Daily Life detection using machine learning, Sensors, 20(13), 3777, 2020. doi: 10.3390/s20133777"</p>
BITS-2 Dataset for Fall Detection
<p>Data was collected using a custom-built wrist-worn end worn on the left wrist. Qualcomm Snapdragon 820c. We used MAX30102 Heart rate and SP02 sensor, MPU6500, which gives 3-axis acceleration, 3-axis linear acceleration and 3-axis gyroscope data and GY273 Magnetometer chip for data collection. All the sensors are interfaced to the SoC via the I2C interface using a Mezzanine board.</p><p>Table, each table having six columns, time-stamp, x-axis data, y-axis data and z-axis data, number of axes and type of sensor(label) except in case of heart-rate where there will be only three columns, time-stamp, beats per minute and label (hrt for heart-rate)</p><p>In this data set, the data was collected from a total of 41 volunteers performing 16 ADLs and 8 Falls. Every activity was repeated for five trials.</p>
Validation of the ADAMO Watch for the Early Detection of Fall Events in Older Patients
ClinicalTrials.gov study NCT04398615. IPD Sharing: NO. Countries: 1. Publications: 4.
Comprehensive Fall Prevention and Detection in Multiple Sclerosis
ClinicalTrials.gov study NCT02583386. IPD Sharing: UNDECIDED. Countries: 1. Publications: 1.
Pedestrian Fall Detection in Various Image Degradation Scenarios Datastes
Open the record for dataset details and reuse information.
Usability Validation of Patient Monitoring Device for Pressure Injury Prevention and Fall Detection
ClinicalTrials.gov study NCT03121144. IPD Sharing: NO. Countries: 1. Publications: 0.
BITS-2 Dataset for Fall detection
<p>Data was collected using a custom built-wrist worn end worn on the left-wrist. Qualcomm Snapdragon 820c. We used MAX30102 Heart rate and SP02 sensor, MPU6500, which gives 3-axis acceleration, 3-axis linear acceleration and 3-axis gyroscope data and GY273 Magnetometer chip for data collection. All the sensors are interfaced to the SoC via the I2C interface using a Mezzanine board.</p> <p>Table, each table having six columns, time-stamp, x axis data, y-axis data and z axis data, number of axis and type of sensor(label) except in case of heart-rate where there will be only three columns, time-stamp, beats per minute and label (hrt for heart-rate)</p> <p>In this data set, the data was collected from a total of 41 volunteers performing 16 ADLs and 8 Falls . Every activity was repeated for 5 trials.</p>
E-vone® Use Detect Falls Among hOspitalized Patients in geRiAtric Medicine
ClinicalTrials.gov study NCT04067193. IPD Sharing: Not stated. Countries: 1. Publications: 0.
INVSENSOR00027 Fall Detection Clinical Performance Study
ClinicalTrials.gov study NCT04224753. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Fall Detection and Prevention for Memory Care Through Real-time Artificial Intelligence Applied to Video
ClinicalTrials.gov study NCT03685240. IPD Sharing: NO. Countries: 1. Publications: 0.
Evaluation of the DETECT'CHUTE CLINIBED Smart Carpet for Elderly People Fall Detection
ClinicalTrials.gov study NCT03455894. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Efficacy of Bed Mattress Sensor for Detecting Pre-fall Activities and Preventing Bedside Falls in Elderly in Residential Setting
ClinicalTrials.gov study NCT05490368. IPD Sharing: YES. Countries: 1. Publications: 0.
A Novel System to Detect Falls in Real-life Conditions
ClinicalTrials.gov study NCT02835248. IPD Sharing: NO. Countries: 1. Publications: 0.
Evaluation of a Simple Clinical Test to Detect the Risk of Falling in Patients With BPCO and Research for Predictive Factors of Fall Risk.
ClinicalTrials.gov study NCT03152344. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Assessment of a New System to Detect, Quantify and Treat Near Falls in Older Adults
ClinicalTrials.gov study NCT01668979. IPD Sharing: Not stated. Countries: 1. Publications: 0.
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
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International Brain Laboratory public data
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OpenNeuro
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