Leveraging AI and Semantic Web Technologies for Enhanced Image Processing in Robotic Systems

Fall detection is an important task in the robotic system, and it is related to the well-being of the human subject. However, it is a challenging task to complete due to the presence of false positives and a lack of contextual awareness. In this context, this paper presents a fall detection model th...

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Bibliographic Details
Published inInternational journal on semantic web and information systems Vol. 21; no. 1; pp. 1 - 19
Main Authors Li, Hao, Chen, Yedong, Xie, Ziming, Ye, Bo, Bansal, Shavi
Format Journal Article
LanguageEnglish
Published Hershey IGI Global 20.06.2025
Subjects
Accuracy
Artificial intelligence
Datasets
Decision making
Deep learning
Fall detection
Image processing
Image retrieval
Information systems
Internet of Things
Knowledge
Knowledge representation
Monitoring systems
Object recognition
Real time
Robotics
Semantic web
Semantics
Smart houses
Well being
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Summary:Fall detection is an important task in the robotic system, and it is related to the well-being of the human subject. However, it is a challenging task to complete due to the presence of false positives and a lack of contextual awareness. In this context, this paper presents a fall detection model that integrates You Only Look Once11—for real-time object detection—with semantic web technologies for intelligent decision-making. The semantic knowledge graph is used to establish a relation between the You Only Look Once11 results and the fall events. Experimental evaluation of the proposed model presents an mAP@0.5 of 0.806, validating the effectiveness of artificial intelligence-driven visual processing combined with semantic knowledge representation for enhanced fall detection in robotic systems.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
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ISSN:1552-6283
1552-6291
DOI:10.4018/IJSWIS.380738