The author presents the RISAR method, utilizing reconfigurable intelligent surfaces to enhance human activity recognition with Wi-Fi signals, achieving high accuracy and efficiency.
The author proposes H-HAR, a new approach to Human Activity Recognition that focuses on hierarchy-aware label relationship modeling to enhance model performance and interpretation.
RISAR significantly enhances human activity recognition accuracy using Wi-Fi signals.
The article presents an Android application that recognizes daily human activities and calculates the calories burned in real-time using smartphone sensors, particularly the accelerometer.
This paper introduces TRIS-HAR, a novel system that leverages transmissive reconfigurable intelligent surfaces (TRIS) and a state-space model called HiMamba to significantly improve the accuracy of through-the-wall human activity recognition using radio frequency signals.
본 논문에서는 투과형 재구성 가능 지능형 표면(TRIS) 기술을 활용하여 복잡한 실내 환경에서 무선 신호 전파를 개선하고, 이를 통해 벽을 통과하는 시나리오에서도 높은 정확도를 달성하는 인간 활동 인식 시스템(TRIS-HAR)을 제안합니다.