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192 results for “human detection”
RoHuCAD: Robots and Humans Collaborative Anomaly Detection
<h1>RoHuCAD: Robots and Humans Collaborative Anomaly Detection</h1> <p>RoHuCAD is a dataset of human-robot collaboration in a robotic workshop (check <code>workshop_layout.png</code>). Two robots (collaborative manipulator - cobot, autonomous mobile robot - AMR) assist three human operators in assembly of electronic devices.</p> <p>There are two 8-min long recordings in the dataset. They mostly follow the same scenario, with slightly different anomalies. The data is in ROS Noetic rosbag format.</p> <h2>Included data </h2> <ul> <li>RGBD camera data (color + depth) <ul> <li>3 cameras: <a href="https://www.intelrealsense.com/depth-camera-d435i/">Intel Realsense D435i</a></li> <li>color and depth data at 6 frames per second</li> <li>Intrinsic calibration data</li> <li>Extrinsic calibration data (positions and orientations)</li> </ul> </li> <li>Information about positions of robots <ul> <li>AMR: <a href="https://www.ez-wheel.com/en/development-kit-for-agv-and-amr">Ez-Wheel SWD® Starter Kit</a></li> <li>Cobot: <a href="https://www.universal-robots.com/products/ur10-robot/">Universal Robots UR10e</a></li> </ul> </li> </ul> <h2>Annotations</h2> <p>Annotations of specific anomalies are included (CSV file with columns: event_id, tstart, tend, event_type, person_id, camera_id)</p> <ul> <li>Gestures / poses <ul> <li>BENT</li> <li>T-POSE (hands horizontally to the sides)</li> <li>L+R-UP (both hands up)</li> <li>RH-UP (right hand up)</li> <li>LH-UP (left hand up)</li> <li>SQUAT</li> <li>HI-POSE (waving)</li> </ul> </li> <li>Unsafe behaviour <ul> <li>Human in robot working area</li> <li>Standing back to (moving) robot</li> <li>Looking at phone</li> <li>Human in the way of AMR</li> </ul> </li> <li>Normal activities <ul> <li>Assembling/Working</li> <li>Loading/unloading AMR</li> </ul> </li> </ul> <h2>ROS topics</h2> <ul> <li><code>/tf </code></li> <li><code>/tf_static</code></li> <li><code>/joint_states</code></li> <li>cam_ws2_box <ul> <li><code>/cam_ws2_box/color/camera_info</code></li> <li><code>/cam_ws2_box/color/image_raw/compressed</code></li> <li><code>/cam_ws2_box/depth_registered/camera_info</code></li> <li><code>/cam_ws2_box/depth_registered/image_rect_raw</code></li> </ul> </li> <li>cam_ta2_ws2 <ul> <li><code>/cam_ta2_ws2/color/camera_info</code></li> <li><code>/cam_ta2_ws2/color/image_raw/compressed</code></li> <li><code>/cam_ta2_ws2/depth_registered/camera_info</code></li> <li><code>/cam_ta2_ws2/depth_registered/image_rect_raw</code></li> </ul> </li> <li>cam_ta1_ws2 <ul> <li><code>/cam_ta1_ws2/color/camera_info</code></li> <li><code>/cam_ta1_ws2/color/image_raw/compressed</code></li> <li><code>/cam_ta1_ws2/aligned_depth_to_color/camera_info</code></li> <li><code>/cam_ta1_ws2/aligned_depth_to_color/image_raw</code></li> </ul> </li> </ul> <h2>Acknowledgement</h2> <p>The work leading to these results has received funding from the European Union’s Horizon Europe research and innovation programme within the ULTIMATE project under the Grant Agreement no 101070162.</p>
A Wi-Fi Channel State Information (CSI) and Received Signal Strength (RSS) data-set for human presence and movement detection
<p>This data-set consists of antenna-wise received signal strength (RSS) and channel state information (CSI) data. Both types of data have been captured using the <a href="https://dhalperi.github.io/linux-80211n-csitool/">Intel CSI Tools</a>. The RSS data have been used in our paper "Detecting Human Movement from Ambient Wi-Fi Signal Strength".</p> <p>This release extends the README with a data dictionary for the annotations. We hope to add more information about the data acquisition process (e.g., data acquisition protocols).</p>
Human papillomavirus (HPV) detection in vaginal self-samples: evaluation of eNat® as an alternative suspension medium to ThinPrep®PreservCyt® for vaginal swabs
<p>Dataset 1: HPV positivity on cervical and vaginal self-samples with different HPV real-time PCR assays</p> <p>Dataset 2: hrHPV viral load in cervical and vaginal self-samples</p> <p>Datakey 1: HPV positivity on cervical and vaginal self-samples with different HPV real-time PCR assays</p> <p>Datakey 2: hrHPV viral load in cervical and vaginal self-samples</p>
A 3D human head dataset for non-coplanar keypoints detection
<p>This MRI volumes ("nii.gz" format) were part of the IXI dataset (<a href="https://brain-development.org/ixi-dataset/">IXI Dataset – Brain Development (brain-development.org)</a>). For our work, we focused on the T1-weighted images, which provided an higher degree of anatomical details. We chose and manually annotated 4 non-coplanar points inside these volumes, in order to train a 3D CNN to detect them. Such points could be exploited to perform alignment tasks. The annotation was carried out using 3D Slicer, and we encourage to use it for the visualization of the volumes and the relative annotations ("json" format). Below a description of the 4 keypoints:</p> <ul> <li>Keypoint 1: The cerebral aqueduct in correspondence of the transverse plane slice where the mammillary bodies are two well defined little balls.</li> <li>Keypoint 2: The point of contact between the two ventricles anterior horns before going into lateral ventricles (visualize on coronal plane)</li> <li>Keypoint 3: The right eye center in correspondence of the largest diameter circle (on the coronal plane)</li> <li>Keypoint 4: The left eye center in correspondence of the largest diameter circle (on the coronal plane)</li> </ul> <p>The dataset consists of 507 volumes with related annotations.</p>
Chromosome images used for "Centromere detection of human metaphase chromosome images using a candidate based method"
<p>Chromosome image data used in the paper "Centromere detection of human metaphase chromosome images using a candidate based method". Images are in tiff format</p>
Chromosome images used for "Centromere detection of human metaphase chromosome images using a candidate based method"
<p>Chromosome image data used in the paper "Centromere detection of human metaphase chromosome images using a candidate based method". Images are in tiff format.</p>
Evaluation of two 4th generation point-of-care assays for the detection of Human Immunodeficiency Virus infection.
<p>Background. Fourth generation assays detect simultaneously antibodies for HIV and the p24 antigen, identifying HIV infection earlier than previous generation tests. Previous studies have shown that the Alere Determine HIV-1/2 Combo has lower than anticipated performance in detecting antibodies for HIV and the p24 antigen. Furthermore, there are currently very few studies evaluating the performance of Standard Diagnostics BIOLINE HIV Ag/Ab Combo.</p> <p>Objective: To evaluate the performance of the Alere Determine HIV-1/2 Combo and the Standard Diagnostics BIOLINE HIV Ag/Ab Combo in a panel of frozen serum samples.</p> <p>Study Design: The testing panel included 133 previously frozen serum specimens from the UCLA Clinical Microbiology & Immunoserology laboratory. Reference testing included testing for HIV antibodies by a 3<sup>rd</sup> generation enzyme immunoassay followed by HIV RNA detection. Antibody negative and RNA positive sera were also tested by a laboratory 4<sup>th</sup> generation HIV Ab/Ag enzyme immunoassay.</p> <p>Results: Reference testing yielded 97 positives for HIV infection and 36 negative samples. Sensitivity of the Alere test was 95% (88-98%), while the SD Bioline sensitivity was 91% (83-96%). Both assays showed 100% (90-100%) specificity. No indeterminate or invalid results were recorded. Among 13 samples with acute infection (HIV RNA positive, HIV antibody negative), 12 were found positive by the first assay and 8 by the second. The antigen component of the Alere assay detected 10 acute samples, while the SD Bioline assay detected only one.</p> <p>Conclusions: Both rapid assays showed very good overall performance in detecting HIV infection in frozen serum samples, but further improvements are required to improve the performance in acute infection.</p>
Detection of Areas with Human Vulnerability Using Public Satellite Images and Deep Learning (Dataset)
<div> <h2>Overview</h2> <a href="https://github.com/fbvidal/HumanVulnerabilityDetectionDL#overview"></a></div> <p>This repository contains the code and resources for the project titled <strong>"Detection of Areas with Human Vulnerability Using Public Satellite Images and Deep Learning"</strong>. The goal of this project is to identify regions where individuals are living under precarious conditions and facing neglected basic needs, a situation often seen in Brazil. This concept is referred to as "human vulnerability" and is exemplified by families living in inadequate shelters or on the streets in both urban and rural areas.</p> <p>Focusing on the Federal District of Brazil as the research area, this project aims to develop two novel public datasets consisting of satellite images. The datasets contain imagery captured at 50m and 100m scales, covering regions of human vulnerability, traditional areas, and improperly disposed waste sites.</p> <p>The project also leverages these datasets for training deep learning models, including <strong>YOLOv7</strong> and other state-of-the-art models, to perform image segmentation. A comparative analysis is conducted between the models using two training strategies: training from scratch with random weight initialization and fine-tuning using pre-trained weights through <strong>transfer learning</strong>.</p> <div> <h3>Key Achievements</h3> <a href="https://github.com/fbvidal/HumanVulnerabilityDetectionDL#key-achievements"></a></div> <ul> <li>Two new satellite image datasets focusing on human vulnerability and improperly disposed waste sites, available in public domains.</li> <li>Comparison of image segmentation models, including <strong>YOLOv7</strong> and <strong>Segmentation Models</strong>, with performance metrics.</li> <li>Best F1-scores: 0.55 for <strong>YOLOv7</strong> and 0.64 for <strong>Segmentation Models</strong>.</li> </ul> <p>This repository provides the code, models, and data pipelines used for training, evaluation, and performance comparison of these deep learning models.<br><br></p> <div> <h2>Citation (Bibtex)</h2> <a href="https://github.com/fbvidal/HumanVulnerabilityDetectionDL?tab=readme-ov-file#citation-bibtex"></a></div> <pre><code>@TECHREPORT {TechReport-Julia-Laura-HumanVulnerability-2024, author = "Julia Passos Pontes, Laura Maciel Neves Franco, Flavio De Barros Vidal", title = "Detecção de Áreas com Atividades de Vulnerabilidade Humana utilizando Imagens Públicas de Satélites e Aprendizagem Profunda", institution = "University of Brasilia", year = "2024", type = "Undergraduate Thesis", address = "Computer Science Department - University of Brasilia - Asa Norte - Brasilia - DF, Brazil", month = "aug", note = "People living in precarious conditions and with their basic needs neglected is an unfortunate reality in Brazil. This scenario will be approached in this work according to the concept of \"human vulnerability\" and can be exemplified through families who live in inadequate shelters, without basic structures and on the streets of urban or rural centers. Therefore, assuming the Federal District as the research scope, this project proposes to develop two new databases to be made available publicly, considering the map scales of 50m and 100m, and composed by satellite images of human vulnerability areas, regions treated as traditional and waste disposed inadequately. Furthermore, using these image bases, trainings were done with the YOLOv7 model and other deep learning models for image segmentation. By adopting an exploratory approach, this work compares the results of different image segmentation models and training strategies, using random weight initialization (from scratch) and pre-trained weights (transfer learning). Thus, the present work was able to reach maximum F1 score values of 0.55 for YOLOv7 and 0.64 for other segmentation models." } </code></pre> <div> </div> <div> <h2>License</h2> <a href="https://github.com/fbvidal/HumanVulnerabilityDetectionDL?tab=readme-ov-file#license"></a></div> <p>This project is licensed under the MIT License - see the LICENSE file for details.</p>
Procalcitonin detection in human plasma specimens using a fast version of proximity extension assay.
<p>Proximity Extension Assay (PEA), an homogeneous, dual-recognition immunoassay, has proven to be sensitive, specific and convenient for detection or quantitation of one or multiple analytes in human plasma. In this paper, the PEA principle was applied to the detection of procalcitonin (PCT), a widely used biomarker for the identification of bacterial infection. A simple, short PEA protocol, with an assay time suitable for point-of-care diagnostics, is presented here as a proof of concept. Pairs of oligonucleotides and monoclonal antibodies were selected to generate tools specifically adapted to the development of an efficient PEA for PCT detection. The assay time was reduced by more than 13-fold compared to published versions of PEA, without significantly affecting assay performance. It was also demonstrated that T4 DNA polymerase could advantageously be replaced by other polymerases having strong 3’>5’ exonuclease activity. The sensitivity of this improved assay was determined to be about 0.1 ng/mL of PCT in plasma specimen.</p>
Does the tail show when the nose knows? AI-enhanced analysis of tail kinematics outperforms human experts at predicting when detection dogs find their target odor
Open the record for dataset details and reuse information.
Detection and analysis of complex structural variation in human genomes across populations and in brains of donors with psychiatric disorders
Open the record for dataset details and reuse information.
Specific monitoring results of ESBL-/AmpC-/carbapenemase-producing bacteria and specific monitoring of carbapenemase-producing bacteria, in the absence of isolates detected complementing the European Union Overview Summary Report on Antimicrobial Resistance in zoonotic and indicator bacteria from humans, animals and food in 2017/2018
<p>This dataset derives from the specific monitoring of E. coli producers of ESBLs/AmpC/carbapenemases, as well as the specific monitoring of carbapenemase-producers (voluntary reporting), in the absence of any isolates detected. This dataset contains only data when Total units positive equals zero '0'.</p>
Genome graphs detect human polymorphisms in active epigenomic states during influenza infection: code and processed data
<p>Manuscript, figure, and analysis code and processed data for the Groza et al (2022) preprint.</p>
Data for: Development of a method for the measurement of human scent samples using comprehensive two-dimensional gas chromatography with mass detection
<p>This dataset was used for development of a method for the measurement of human scent samples using comprehensive two-dimensional gas chromatography with mass detection [<a href="https://doi.org/10.1016/j.forsciint.2016.09.011">https://doi.org/10.1016/j.forsciint.2016.09.011</a>].</p> <p>The dataset contains chromatograms of a model mixture of human scent and chromatograms of the human scent of one volunteer measured on different column setups. Each sample was processed in ChromaToF(version 4.72.0.0) by LECO corp. The processing step was executed at the signal-to-noise (SN) ratio levels 100, 300, and 500 (human scent samples chromatograms).</p>
Data from: Early detection of human impacts using acoustic monitoring: an example with forest elephants
<p>The impacts of human activities and climate change on animal populations often take considerable time before they are reflected in typical measures of population health such as population size, demography, and landscape use. Earlier detection of such impacts could enhance the effectiveness of conservation strategies, particularly for species with slow population growth. Passive acoustic monitoring is increasingly used to estimate occupancy and population size, but this tool can also monitor subtle shifts in behavior that might be early indicators of changing impacts. Here we use data from an acoustic grid, monitoring 1250 km<sup>2</sup> of forest in the northern Republic of Congo, to study how forest elephants (<em>Loxodonta cyclotis</em>) assess the risk of poaching across a landscape that includes a national park as well as active and inactive logging concessions. By quantifying emerging patterns of behavior at the population level, arising from individual-based decisions, we gain an understanding of how elephants perceive their landscape along an axis of human disturbance. Forest elephants in relatively undisturbed forests are active nearly equally day and night. However, they become more nocturnal when exposed to a perceived risk such as poaching. We assessed elephant perception of risk by monitoring changes in the likelihood of nocturnal activity relative to differing levels of human activity. We show that logging is perceived to be a risk on short-time and small spatial scales but with little effect on animal density. However, risk avoidance persisted in areas with relatively easy access to poachers and in more open habitats where poaching has historically been concentrated. Increased nocturnal activity is a common response in many animals to human intrusion on the landscape. Provided a species is acoustically active, passive acoustic monitoring can measure changes in human impact at the early stages of such change, informing management priorities.</p>
Bridging the Chromosome-Centric and Biology and Disease Human Proteome Projects: Accessible and automated tools for interpreting biological and pathological impact of protein sequence variants detected via proteogenomics
<p>Bridging the Chromosome-Centric and Biology and Disease Human Proteome Projects: Accessible and automated tools for interpreting biological and pathological impact of protein sequence variants detected via proteogenomics</p>
Fig. 1 in Detection of rodent-borne parasitic pathogens of wild rats in Serdang, Selangor, Malaysia: A potential threat to human health
Fig. 1. The study area, Universiti Putra Malaysia showing the study sites circled in black.
Dataset for "Detection and quantification of ergothioneine in human serum using surface enhanced Raman scattering (SERS)"
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A supramolecular cucurbit[8]uril-based rotaxane chemosensor for the optical tryptophan detection in human serum and urine
<p>Dataset of the publication data. Raw and analyzed data included in each excel file.</p>
Detecting ecological traps in human-altered landscapes: A case study of the thick-billed longspur nesting in croplands
<p>Conversion of the North American prairies to cropland remains a prominent threat to grassland bird populations. Yet, a few species nest in these vastly modified systems. The thick-billed longspur (<em>Rhynchophanes mccownii</em>) is an obligate grassland bird whose populations have declined 4% annually during the past 50 years. Thick-billed longspurs historically nested in recently disturbed or sparsely vegetated patches within native mixed-grass prairie, but observations of longspurs in spring cereal and pulse crop fields during the breeding season in northeastern Montana, USA suggest such fields also provide cues for habitat selection. Maladaptive selection for poor-quality habitat may contribute to ongoing declines in longspur populations, but information on thick-billed longspur breeding ecology in crop fields is lacking. We hypothesized that these crop fields may function as ecological traps; specifically, we expected that crop fields may provide cues for territory selection, but frequent human disturbance and increased exposure to weather and predators would have negative consequences for reproduction. To address this hypothesis, we compared measures of habitat selection (settlement patterns and trends in abundance) and productivity (nest density, nest survival, and number of young fledged) between crop fields and native grassland sites during 2020–21. Across both years, settlement patterns were similar between site types and occupancy ranged from 0.52 ± 0.17SE to 0.99 ± 0.01 on April 7 and 30, respectively. Early season abundance differed by year, and changes in abundance during the breeding season appeared to be associated with precipitation-driven vegetation conditions rather than habitat type. While an index of nest density was lower in crop than native sites, the number of young fledged per successful nest (2.9 ± 0.18SE) and nest survival (0.24 ± 0.03 SE; n=222 nests) were similar for crop and native sites. Collectively, the data did not support our ecological trap hypothesis: longspurs did not exhibit a clear preference for crop sites and reproductive output was not significantly reduced. Our results indicate that croplands may provide alternative breeding habitat within a human-dominated landscape.</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.