Real-time Semantic Healthcare System: Visual Risks Identification for Elders and Children - Belkebir, Malak; Maarouk, Toufik Messaoud; Nini, Brahim (Informatica, 2024)
Deep learning and data-driven approaches are commonly used to avoid accidents involving elders and children. However, existing models are limited by a semantic gap, hindering their ability to infer new risks that have not been previously trained. In this paper, a realtime healthcare system is developed to identify and infer visual risks in surveillance videos for elders and children. The system consists of three main modules: "visual information extraction," which leverages advancements in artificial vision techniques such as GCD, YOLO, ResNet18, and IoU; "ontology modeling," where a new high-level ontology named "Risks-Identification-Onto" is constructed based on FOL and DLs; and "risk identification," where the system infers risks by deducing new knowledge through the reasoning of generated formal rules over the data-driven techniques. Additionally, the system generates a high-level semantic description of the risky situation. Four common risk scenarios - "Hurt," "Burn," "Existing-in-dangerous-places," and "Hit"- are selected to evaluate the effectiveness of the proposed system. Evaluation is conducted using the Charades and A2D datasets, demonstrating the system's efficiency in identifying and inferring risks in realtime with an accuracy ranging from 97.61% to 99.43%.