This data article presents a dataset of 11,884 labeled images documenting a simulated blood extraction (phlebotomy) procedure performed on a training arm. Images were extracted from high-definition videos recorded under controlled conditions and curated to reduce redundancy using Structural Similarity Index Measure (SSIM) filtering. An automated face-anonymization step was applied to all videos prior to frame selection. Each image contains polygon annotations for five medically relevant classes: syringe, rubber band, disinfectant wipe, gloves, and training arm. The annotations were exported in a segmentation format compatible with modern object detection frameworks (e.g., YOLOv8), ensuring broad usability. This dataset is partitioned into training (70%), validation (15%), and test (15%) subsets and is designed to advance research in medical training automation and human-object interaction. It enables multiple applications, including phlebotomy tool detection, procedural step recognition, workflow analysis, conformance checking, and the development of educational systems that provide structured feedback to medical trainees. The data and accompanying label files are publicly available on Zenodo.

翻译：本文介绍一个包含11,884张标注图像的数据集，该数据集记录了在训练手臂上进行的模拟采血（静脉穿刺）操作流程。所有图像均从受控条件下录制的高清视频中提取，并采用结构相似性指数（SSIM）过滤方法进行冗余度优化处理。在帧选择前，所有视频均经过自动化人脸匿名化处理。每张图像包含五个医学相关类别的多边形标注：注射器、止血带、消毒棉片、手套和训练手臂。标注以兼容现代目标检测框架（如YOLOv8）的分割格式导出，确保了广泛的适用性。本数据集按70%训练集、15%验证集和15%测试集划分，旨在推动医学培训自动化与人-物交互领域的研究。其支持多种应用场景，包括静脉穿刺工具检测、操作步骤识别、工作流程分析、合规性检查，以及可为医学学员提供结构化反馈的教育系统开发。数据及配套标注文件已公开发布于Zenodo平台。