인문학
사회과학
자연과학
공학
의약학
농수해양학
예술체육학
복합학
DBpia AI
AI 에이전트
AI 아이디어
AI 리더
AI 뷰어
Citeasy
크롬 서지관리 다운로드
지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
피인용 0
초록·키워드
This paper presents an edge-based system for detecting post-fall non-recovery states in a predefined indoor monitoring area. The target of the proposed system is not the instantaneous fall event itself, but a fall-like abnormal posture that persists without recovery. Human keypoints and a person bounding box are extracted using a pretrained YOLO26n-Pose model. Torso orientation is used as the primary posture feature, and the bounding-box width-to-height ratio is used as an auxiliary horizontal-posture feature. A finite-state machine separates frame-level abnormal-posture candidates from emergency decisions by assigning Normal, Warning, and Emergency states according to the accumulated posture duration. In the self-recorded scenario evaluation, no Emergency transition was observed in the normal-posture and daily-motion scenarios, whereas sustained abnormal postures produced sequential Warning-to-Emergency transitions. The average pose inference time on the Jetson AGX Orin was 53.12 ms/frame. The UR Fall Detection Dataset, consisting of 30 fall and 40 ADL RGB clips, was additionally used to examine the external applicability of the posture features and their threshold sensitivity. Under the conservative sensitivity setting, abnormal-posture candidates were detected in 90.0% of the fall clips, while the moderate Warning setting achieved a clip-level F1-score of 0.6552. These results should be interpreted as a feasibility and sensitivity analysis under the evaluated conditions rather than as a general performance validation for unrestricted fall-detection environments.
본문·목차
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