The DOI displayed above is for this specific version of this dataset, which is currently the latest. Newer versions may be published in the future. For a link that will always point to the latest version, please use
DOI: 10.4121/30e3b03c-6e82-47d1-a952-7dec811e71a6

Butler, Rick (2025): Code to detect human actions from history vectors. Version 1. 4TU.ResearchData. software. https://doi.org/10.4121/30e3b03c-6e82-47d1-a952-7dec811e71a6.v1

Categories

• Artificial Intelligence and Image Processing
• Information Systems
• Information and Computing Sciences

Keywords

action recognition Group activity recognition History vectors Human 2D poses workflow analysis Workflow phase recognition

Licence

MIT

Collection

• Video-based workflow monitoring for efficiency in the cardiac catheterization laboratory, an observational study

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by Rick Butler

About 200 coronary angiograms were recorded from a distance in a cardiac catheterisation laboratory at the Reinier de Graaf Gasthuis, Delft, NL.

The purpose of the video recordings was to analyse workflow during procedures.

This Python repository aims to recognise workflow phases from human motion.

It analyses bodypart motion, human posture, and positioning with respect to other persons.

After encoding all these aspect into history vectors, it builds a mixture model for classification of new motions.

Additionally, it takes procedure duration into account.

Unfortunately, this approach proved unable to accurately classify workflow phases, so using this code for that purpose is not recommended.

History

• 2025-04-29 first online, published, posted

Publisher

4TU.ResearchData

Code hosting project url

https://gitlab.tudelft.nl/medical-process-engineering/history-vector-phase-detection.git

Organizations

TU Delft, Faculty of Mechanical Engineering, Department of Biomechanical Engineering, Medical Process Engineering