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.
초록·키워드
Smart walkers that provide walking assistance for the elderly or disabled increasingly require the ability to recognize the condition of the user and realize real-time interactions. Among the core components of such systems, recognizing the walking intention and estimating the position of the user are essential. This study proposes a technique that simultaneously performs walking-intention recognition and position estimation using depth image sequences acquired from a single time-of-flight depth camera. For user-walking- intention recognition, the depth image sequence is used as the input to a transformer model. This enables the network to learn spatial depth features and temporal walking patterns in an end-to-end manner, allowing it to predict walking intention. For user-position estimation, pixel-level depth variation analysis based on depth differences is applied to quantitatively classify user movements. A Kalman filter is then employed to enhance the stability of distance estimation between humans and walking devices. The experimental results achieved an F1-score of approximately 74% for walking-intention prediction and an average position error of 0.723 ± 0.027 m for distance estimation. The proposed method can serve as a core perception and intention recognition technology for smart assistive- walker control. Future research will aim to further enhance the prediction performance of the developed system by integrating motion trajectory data from smart walkers.
본문·목차
인공지능 문자 인식 모델을 통해 추출된 텍스트로, 일부 오타나 오류가 포함될 수 있으나 지속적으로 개선 중입니다.
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UCI(KEPA) : I410-151-26-02-096087977
