ArticleProceedings. IEEE International Conference on Healthcare Informatics2019
Multimodal Attention Network for Trauma Activity Recognition from Spoken Language and Environmental Sound.
Article in Proceedings. IEEE International Conference on Healthcare Informatics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
5 citing papers in PubMed.
- Focusing on What Matters: Fine-grained Medical Activity Recognition for Trauma Resuscitation via Actor Tracking.Conference on Computer Vision and Pattern Recognition Workshops. IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Workshops · 2024Article
- Real-time Context-Aware Multimodal Network for Activity and Activity-Stage Recognition from Team Communication in Dynamic Clinical Settings.Proceedings of the ACM on interactive, mobile, wearable and ubiquitous technologies · 2023Article
- Multi-dimensional task recognition for human-robot teaming: literature review.Frontiers in robotics and AI · 2023Review
- Speech-Based Activity Recognition for Trauma Resuscitation.Proceedings. IEEE International Conference on Healthcare InformaticsArticle
- Video-based Concurrent Activity Recognition for Trauma Resuscitation.Proceedings. IEEE International Conference on Healthcare InformaticsArticle
Corrections and comments
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Authors and funding
8 authors.
Funding
Abstract
Trauma activity recognition aims to detect, recognize, and predict the activities (or tasks) during a trauma resuscitation. Previous work has mainly focused on using various sensor data including image, RFID, and vital signals to generate the trauma event log. However, spoken language and environmental sound, which contain rich communication and contextual information necessary for trauma team cooperation, are still largely ignored. In this paper, we propose a multimodal attention network (MAN) that uses both verbal transcripts and environmental audio stream as input; the model extracts textual and acoustic features using a multi-level multi-head attention module, and forms a final shared representation for trauma activity classification. We evaluated the proposed architecture on 75 actual trauma resuscitation cases collected from a hospital. We achieved 72.4% accuracy with 0.705 F1 score, demonstrating that our proposed architecture is useful and efficient. These results also show that using spoken language and environmental audio indeed helps identify hard-to-recognize activities, compared to previous approaches. We also provide a detailed analysis of the performance and generalization of the proposed multimodal attention network.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.