Büsch, LukasLukasBüschPalazoğlu, MertMertPalazoğluSchüppstuhl, ThorstenThorstenSchüppstuhl2025-08-062025-08-06202520th Global Conference on Sustainable Manufacturing (GCSM 2024)9783031938900https://hdl.handle.net/11420/56518This 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.enhttps://creativecommons.org/licenses/by/4.0/Artificial Intelligence | Assembly | Azure Kinect | Dataset | Human Action Recognition | Methods-Time MeasurementComputer Science, Information and General Works::006: Special computer methodsConference Paperhttps://doi.org/10.15480/882.1543710.1007/978-3-031-93891-7_3310.15480/882.15437Conference Paper