Summary of Real-time Multimodal Cognitive Assistant For Emergency Medical Services, by Keshara Weerasinghe et al.
Real-Time Multimodal Cognitive Assistant for Emergency Medical Services
by Keshara Weerasinghe, Saahith Janapati, Xueren Ge, Sion Kim, Sneha Iyer, John A. Stankovic, Homa Alemzadeh
First submitted to arxiv on: 11 Mar 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computation and Language (cs.CL); Computer Vision and Pattern Recognition (cs.CV)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper presents CognitiveEMS, a wearable cognitive assistant system that assists Emergency Medical Services (EMS) responders in making rapid decisions under time-sensitive conditions. The system processes real-time multimodal data from emergency scenes using Augmented Reality smart glasses, providing assistance in EMS protocol selection and intervention recognition. The authors introduce three novel components: a Speech Recognition model fine-tuned for medical emergency conversations; an EMS Protocol Prediction model combining tiny language models with EMS domain knowledge; and an EMS Action Recognition module inferring treatment actions taken by responders. Results show superior performance compared to state-of-the-art methods in speech recognition, protocol prediction, and action recognition, while maintaining low latency. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps emergency responders make better decisions during medical emergencies. It creates a special tool that works with virtual reality glasses to analyze what’s happening at the scene and suggest the best actions to take. The tool is made up of three parts: one that recognizes speech, another that predicts the right medical procedures, and one that figures out what actions are being taken. Tests show it does better than other methods in these areas, while still working quickly enough to help responders make decisions fast. |
