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
Wearable wristband systems leverage deep learning to revolutionize hand gesture recognition in daily activities. Unlike existing approaches that often focus on static gestures and require extensive labeled data, the proposed wearable wristband with self-supervised contrastive learning excels at dynamic motion tracking and adapts rapidly across multiple scenarios. It features a four-channel sensing array composed of an ionic hydrogel with hierarchical microcone structures and ultrathin flexible electrodes, resulting in high-sensitivity capacitance output. Through wireless transmission from a Wi-Fi module, the proposed algorithm learns latent features from the unlabeled signals of random wrist movements. Remarkably, only few-shot labeled data are sufficient for fine-tuning the model, enabling rapid adaptation to various tasks. The system achieves a high accuracy of 94.9% in different scenarios, including the prediction of eight-direction commands, and air-writing of all numbers and letters. The proposed method facilitates smooth transitions between multiple tasks without the need for modifying the structure or undergoing extensive task-specific training. Its utility has been further extended to enhance human-machine interaction over digital platforms, such as game controls, calculators, and three-language login systems, offering users a natural and intuitive way of communication.
인공지능 문자 인식 모델을 통해 추출된 텍스트로, 일부 오타나 오류가 포함될 수 있으나 지속적으로 개선 중입니다.
오류를 발견하셨다면 해당 부분을 드래그한 후 ' 를 통해 신고해주세요.
오류를 발견하셨다면 해당 부분을 드래그한 후 ' 를 통해 신고해주세요.
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