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This paper introduces a novel Human Action Recognition (HAR) dataset designed to improve Human-Robot Collaboration (HRC) in green electrolyzer production. Recorded in a lab using RGB, depth, and skeletal data from Azure Kinect, the dataset focuses on assembly tasks, labeled with Methods-Time Measurement (MTM) primitives. The use of a green screen enables the study of background effects on HAR algorithms. The dataset addresses the challenges of data imbalance and limited training data in industrial HAR applications, offering standardized, mergeable, and extendable data for the research community. It aims to enhance the development of HAR algorithms in manufacturing contexts, with future plans for collaborative expansion and real-world application. The dataset and further information are available via a GitHub repository.

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HARDAT: Human Action Recognition Dataset for Manual Assembly Tasks

  • 会议论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年

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  • Lukas Büsch,
  • Mert Palazoğlu,
  • Thorsten Schüppstuhl

摘要

This paper introduces a novel Human Action Recognition (HAR) dataset designed to improve Human-Robot Collaboration (HRC) in green electrolyzer production. Recorded in a lab using RGB, depth, and skeletal data from Azure Kinect, the dataset focuses on assembly tasks, labeled with Methods-Time Measurement (MTM) primitives. The use of a green screen enables the study of background effects on HAR algorithms. The dataset addresses the challenges of data imbalance and limited training data in industrial HAR applications, offering standardized, mergeable, and extendable data for the research community. It aims to enhance the development of HAR algorithms in manufacturing contexts, with future plans for collaborative expansion and real-world application. The dataset and further information are available via a GitHub repository.