Baseline Models for Action Recognition of Unscripted Casualty Care Dataset

Nina Jiang, Yupeng Zhuo, Andrew W. Kirkpatrick, Kyle Couperus, Oanh Tran, Jonah Beck, Deanna Devane, Ross Candelore, Jessica McKee, Chad Gorbatkin, Eleanor Birch, Christopher Colombo, Bradley Duerstock, Juan Wachs*

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review


This paper presents a comprehensive framework of datasets and algorithms for action recognition in scenarios where data is scarce, unstructured, and unscripted. The long-term objective of this work is an intelligent assistant to the medic, a surrogate buddy, that can tell the medic what needs to get done in every step of trauma resuscitation. As an essential part of this objective, we collected datasets and developed algorithms suitable for emergent contexts, such as casualty care in the field, disaster response and recovery scenarios, and other related high-risks/high-stakes scenarios where real-time decision-making is crucial. The proposed framework enables the development of new algorithms by providing a standardized set of evaluation metrics and test cases for assessing their performance. Ultimately, this research seeks to enhance the capabilities of practitioners and emergency responders by enabling them to better anticipate and recognize actions in challenging and unpredictable situations. Our dataset, referred to as Trauma Thompson, includes Tourniquet Application, Tracheostomy, Tube Thoracostomy, Needle Thoracostomy, and Interosseous Insertion procedures. The proposed algorithms based on the relative position embedding for the Vision Transformer referred as to ReVit, can achieve competitive performance with the state-of-art algorithms on our dataset.

Original languageEnglish
Title of host publicationMedical Image Understanding and Analysis - 27th Annual Conference, MIUA 2023, Proceedings
EditorsGordon Waiter, Georgios Leontidis, Teresa Morris, Tryphon Lambrou, Nir Oren, Sharon Gordon
PublisherSpringer Science and Business Media Deutschland GmbH
Number of pages13
ISBN (Print)9783031485923
StatePublished - 2024
Externally publishedYes
Event27th Annual Conference on Medical Image Understanding and Analysis, MIUA 2023 - Aberdeen, United Kingdom
Duration: 19 Jul 202321 Jul 2023

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume14122 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference27th Annual Conference on Medical Image Understanding and Analysis, MIUA 2023
Country/TerritoryUnited Kingdom


  • Action anticipation
  • Action recognition
  • Combat Casualty Care
  • Egocentric datasets
  • Life-saving interventions
  • Surgical simulation


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