The content discusses the development of CognitiveEMS, an end-to-end wearable cognitive assistant system for EMS responders. It addresses challenges in speech recognition, protocol prediction, and intervention recognition using multimodal data. The system aims to provide real-time support to EMS responders by leveraging edge computing and advanced AI models.
The paper highlights the importance of accurate protocol selection and rapid decision-making in time-sensitive emergency situations. It introduces novel components like Speech Recognition model fine-tuned for medical emergency conversations and EMS Protocol Prediction model combining state-of-the-art language models with domain knowledge.
Challenges such as unreliable communication, noisy sensor data, and real-time assistance at the edge are discussed along with proposed solutions. The system's performance is evaluated using various datasets and metrics to demonstrate its effectiveness in supporting EMS responders during critical incidents.
Overall, the content showcases the potential of CognitiveEMS to enhance decision-making and intervention processes for EMS responders through real-time cognitive assistance using cutting-edge technology.
Til et andet sprog
fra kildeindhold
arxiv.org
Vigtigste indsigter udtrukket fra
by Keshara Weer... kl. arxiv.org 03-12-2024
https://arxiv.org/pdf/2403.06734.pdfDybere Forespørgsler