ArticleUbiquitous Computing, Electronics & Mobile Communication Conference (UEMCON), IEEE Annual2016
Online Process Phase Detection Using Multimodal Deep Learning.
Article in Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON), IEEE Annual, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
9 citing papers in PubMed.
- 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
- State-of-the-art of situation recognition systems for intraoperative procedures.Medical & biological engineering & computing · 2022Review
- Real-time medical phase recognition using long-term video understanding and progress gate method.Medical image analysis · 2021Article
- Multimodal Attention Network for Trauma Activity Recognition from Spoken Language and Environmental Sound.Proceedings. IEEE International Conference on Healthcare Informatics · 2019Article
- Region-based Activity Recognition Using Conditional GAN.Proceedings of the ... ACM International Conference on Multimedia, with co-located Symposium & Workshops. ACM International Conference on Multimedia · 2017Article
- Progress Estimation and Phase Detection for Sequential Processes.Proceedings of the ACM on interactive, mobile, wearable and ubiquitous technologies · 2017Article
- Language-Based Process Phase Detection in the Trauma Resuscitation.Proceedings. IEEE International Conference on Healthcare Informatics · 2017Article
- Speech Intention Classification with Multimodal Deep Learning.Advances in artificial intelligence. Canadian Society for Computational Studies of Intelligence. Conference · 2017Article
- Poster Abstract: 3D Activity Localization With Multiple Sensors.IPSN : [proceedings]. IPSN (Conference) · 2017Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
Abstract
We present a multimodal deep-learning structure that automatically predicts phases of the trauma resuscitation process in real-time. The system first pre-processes the audio and video streams captured by a Kinect's built-in microphone array and depth sensor. A multimodal deep learning structure then extracts video and audio features, which are later combined through a "slow fusion" model. The final decision is then made from the combined features through a modified softmax classification layer. The model was trained on 20 trauma resuscitation cases (>13 hours), and was tested on 5 other cases. Our results showed over 80% online detection accuracy with 0.7 F-Score, outperforming previous systems.
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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.