A Spatio-Temporal Deep Learning Approach for Edge Deployable Camera-Based Detection of Cardiac Arrest Symptoms Using Generative-AI Based Synthetic Data

This research develops an edge-deployable camera-based system for detecting visible cardiac-arrest-related symptoms using Generative-AI-based synthetic video data and limited real recordings. Four spatio-temporal deep learning models were comparatively evaluated, achieving up to 98.11% accuracy, while a lightweight CNN-LSTM model was deployed on a Raspberry Pi 4 for real-time symptom monitoring and alert generation.