<![CDATA[Background: Young children commonly place objects near or into their mouths, creating safety concerns that require constant supervision. Specific Background: Advances in computer vision enable real-time recognition of hands, faces, and objects, allowing automated identification of behaviors that may lead to hazardous mouth-related interactions. Knowledge Gap: Few systems combine object detection and human pose estimation specifically to assess risks related to toddler hand–mouth–object interactions in real-time environments. Aims: This study develops a risk detection system using YOLOv11n to recognize hands, faces, and objects while classifying conditions into safe or risky based on Euclidean distance between hand and mouth keypoints. Results: The system produces 92% accuracy in scenarios without objects and 74% in scenarios with objects, demonstrating its capability to differentiate between safe and risky conditions. Novelty: This research introduces an integrated spatial analysis approach that evaluates real-time proximity among hands, mouth, and objects, rather than detecting these elements independently. Implication: The system provides practical potential for real-time child safety monitoring, offering earlier awareness of mouth-related object risks in various activity settings. Highlights • The system identifies hand–mouth proximity to classify safe and risky situations.• Integrated pose estimation and object detection enable spatial risk assessment in real time.• The model supports early awareness of mouth-related object risks during toddler activities. Keywords Object Detection, Pose Estimation, Computer Vision, YOLO, Child Safety Monitoring]]>
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