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The images were extracted from third-person videos and depict a sequence of standard medical actions, such as applying a tourniquet, disinfecting the skin, and inserting a syringe. The dataset is designed to facilitate computer vision tasks in the medical field, specifically those related to human-object interaction (HOI).\n\nThe annotations follow the YOLOv8 (You Only Look Once) format, and each image includes bounding boxes corresponding to key objects involved in the procedure. The goal is to provide a resource for training and evaluating object detection models in simulated clinical environments, thereby supporting intelligent feedback systems for medical training.\n\n \n\nPurpose\n\nThe primary objective of this dataset is to support the development of:\n\n-       Detecting medical objects in clinical scenarios\n\n-       Understanding scenes in training simulations\n\n-       Recognizing human-object interactions during medical procedures\n\n-       Assessing procedural skill and guiding learners using deep learning models\n\nThis dataset is aimed at researchers and practitioners in medical AI, computer vision, and simulation-based training.\n\n \n\nDataset Overview\n\n-       Total images: 11,884\n\n-       Image resolution: Variable, with longer side 640 px (aspect ratio preserved; e.g., 640×480, 640×360).\n\n-       Format: JPEG files with corresponding YOLOv8 `.txt` annotations\n\n-       Annotation type: Bounding boxes in normalized YOLOv8 format\n\n \n\nClasses and Label Counts\n\n-       Class 0: Syringe – 9,949 instances\n\n-       Class 1: Elastic Band – 6,588 instances\n\n-       Class 2: Disinfectant Wipe – 1,152 instances\n\n-       Class 3: Training Arm – 28,370 instances\n\n-       Class 4: Gloves – 17,168 instances\n\nData Split\n\n-       Training set: 70%\n\n-       Validation set: 15%\n\n-       Test set: 15%\n\nThe split is provided as a suggestion and can be adjusted depending on experimental needs.","descriptionType":"Abstract"}],"geoLocations":[],"fundingReferences":[{"funderName":"Swiss National Science Foundation","awardNumber":"208497","funderIdentifier":"10.13039/501100001711","funderIdentifierType":"Crossref Funder ID"}],"xml":"<?xml version="1.0" encoding="UTF-8"?>
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  <identifier identifierType="DOI">10.5281/ZENODO.16924785</identifier>
  <creators>
    <creator>
      <creatorName nameType="Personal">Jimenez Cruz, Raul</creatorName>
      <givenName>Raul</givenName>
      <familyName>Jimenez Cruz</familyName>
      <nameIdentifier nameIdentifierScheme="ORCID" schemeURI="">0000-0002-0154-4865</nameIdentifier>
      <affiliation>Instituto Tecnologico de Estudios Superiores de Monterrey Campus Puebla</affiliation>
    </creator>
    <creator>
      <creatorName nameType="Personal">González  González, Mauricio Jacobo</creatorName>
      <givenName>Mauricio Jacobo</givenName>
      <familyName>González  González</familyName>
      <nameIdentifier nameIdentifierScheme="ORCID" schemeURI="">0000-0002-4001-5026</nameIdentifier>
    </creator>
    <creator>
      <creatorName nameType="Personal">Torres-Huitzil, Cesar</creatorName>
      <givenName>Cesar</givenName>
      <familyName>Torres-Huitzil</familyName>
      <nameIdentifier nameIdentifierScheme="ORCID" schemeURI="">0000-0002-8980-0615</nameIdentifier>
      <affiliation affiliationIdentifier="https://ror.org/03ayjn504" affiliationIdentifierScheme="ROR">Tecnológico de Monterrey</affiliation>
    </creator>
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      <creatorName nameType="Personal">Franceschetti, Marco</creatorName>
      <givenName>Marco</givenName>
      <familyName>Franceschetti</familyName>
      <nameIdentifier nameIdentifierScheme="ORCID" schemeURI="">0000-0001-7030-282X</nameIdentifier>
      <affiliation affiliationIdentifier="https://ror.org/0561a3s31" affiliationIdentifierScheme="ROR">University of St. Gallen</affiliation>
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      <creatorName nameType="Personal">Seiger, Ronny</creatorName>
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      <familyName>Seiger</familyName>
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      <affiliation affiliationIdentifier="https://ror.org/0561a3s31" affiliationIdentifierScheme="ROR">University of St. Gallen</affiliation>
    </creator>
    <creator>
      <creatorName nameType="Personal">García-Bañuelos, Luciano</creatorName>
      <givenName>Luciano</givenName>
      <familyName>García-Bañuelos</familyName>
      <nameIdentifier nameIdentifierScheme="ORCID" schemeURI="">0000-0001-9076-903X</nameIdentifier>
      <affiliation affiliationIdentifier="https://ror.org/03ayjn504" affiliationIdentifierScheme="ROR">Tecnológico de Monterrey</affiliation>
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    <creator>
      <creatorName nameType="Personal">Weber, Barbara</creatorName>
      <givenName>Barbara</givenName>
      <familyName>Weber</familyName>
      <nameIdentifier nameIdentifierScheme="ORCID" schemeURI="">0000-0002-6004-4860</nameIdentifier>
      <affiliation>University of St. Gallen</affiliation>
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  </creators>
  <titles>
    <title>Labeled Images of a Simulated Phlebotomy Procedure on a Training Arm</title>
  </titles>
  <publisher>Zenodo</publisher>
  <publicationYear>2025</publicationYear>
  <resourceType resourceTypeGeneral="Dataset"/>
  <subjects>
    <subject subjectScheme="MeSH">Education, Medical</subject>
    <subject subjectScheme="MeSH">Machine Learning</subject>
    <subject subjectScheme="EuroSciVoc">Computer vision</subject>
    <subject>Medical Procedure</subject>
    <subject>Blood extraction</subject>
    <subject>Human object interaction</subject>
    <subject subjectScheme="GEMET">Image classification</subject>
    <subject subjectScheme="EuroSciVoc">Basic medicine</subject>
    <subject subjectScheme="MeSH">Students, Nursing</subject>
    <subject subjectScheme="MeSH">Students, Medical</subject>
    <subject>yolo</subject>
  </subjects>
  <contributors>
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      <contributorName nameType="Organizational">Tecnológico de Monterrey</contributorName>
      <nameIdentifier nameIdentifierScheme="ROR" schemeURI="">03ayjn504</nameIdentifier>
      <nameIdentifier nameIdentifierScheme="ISNI" schemeURI="">0000 0001 2203 4701</nameIdentifier>
    </contributor>
    <contributor contributorType="HostingInstitution">
      <contributorName nameType="Organizational">University of St. Gallen</contributorName>
      <nameIdentifier nameIdentifierScheme="ROR" schemeURI="">0561a3s31</nameIdentifier>
      <nameIdentifier nameIdentifierScheme="ISNI" schemeURI="">0000 0001 2156 6618</nameIdentifier>
    </contributor>
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  <dates>
    <date dateType="Issued">2025-08-22</date>
  </dates>
  <language>eng</language>
  <relatedIdentifiers>
    <relatedIdentifier relatedIdentifierType="DOI" relationType="HasVersion">10.5281/zenodo.16924786</relatedIdentifier>
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  <rightsList>
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  </rightsList>
  <descriptions>
    <description descriptionType="Abstract">Description

This dataset contains a collection of 11,884 annotated images derived from a simulated blood drawing procedure performed on a medical training arm. The images were extracted from third-person videos and depict a sequence of standard medical actions, such as applying a tourniquet, disinfecting the skin, and inserting a syringe. The dataset is designed to facilitate computer vision tasks in the medical field, specifically those related to human-object interaction (HOI).

The annotations follow the YOLOv8 (You Only Look Once) format, and each image includes bounding boxes corresponding to key objects involved in the procedure. The goal is to provide a resource for training and evaluating object detection models in simulated clinical environments, thereby supporting intelligent feedback systems for medical training.

 

Purpose

The primary objective of this dataset is to support the development of:

-       Detecting medical objects in clinical scenarios

-       Understanding scenes in training simulations

-       Recognizing human-object interactions during medical procedures

-       Assessing procedural skill and guiding learners using deep learning models

This dataset is aimed at researchers and practitioners in medical AI, computer vision, and simulation-based training.

 

Dataset Overview

-       Total images: 11,884

-       Image resolution: Variable, with longer side 640 px (aspect ratio preserved; e.g., 640×480, 640×360).

-       Format: JPEG files with corresponding YOLOv8 `.txt` annotations

-       Annotation type: Bounding boxes in normalized YOLOv8 format

 

Classes and Label Counts

-       Class 0: Syringe – 9,949 instances

-       Class 1: Elastic Band – 6,588 instances

-       Class 2: Disinfectant Wipe – 1,152 instances

-       Class 3: Training Arm – 28,370 instances

-       Class 4: Gloves – 17,168 instances

Data Split

-       Training set: 70%

-       Validation set: 15%

-       Test set: 15%

The split is provided as a suggestion and can be adjusted depending on experimental needs.</description>
  </descriptions>
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    <fundingReference>
      <funderName>Swiss National Science Foundation</funderName>
      <funderIdentifier funderIdentifierType="Crossref Funder ID">10.13039/501100001711</funderIdentifier>
      <awardNumber>208497</awardNumber>
    </fundingReference>
  </fundingReferences>
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