Shouwang Huang
Papers
1
Total Citations
12
H-Index
1
About
Shouwang Huang is a researcher advancing the field of acoustic sensing and multimodal human identification. His work centers on the intersection of audio signal processing, sensor fusion, and person recognition, with a particular focus on footstep-based identification and localization. Huang’s most notable contribution is the development of the AFPILD dataset—a pioneering acoustic footstep collection that integrates data from a single microphone array and LiDAR sensor. This resource, published in 2023, has already garnered 12 citations, underscoring its immediate relevance to the research community. By enabling robust person identification and spatial localization in real-world environments, Huang’s work addresses critical challenges in security, smart environments, and human-computer interaction. His approach leverages the complementary strengths of acoustic and LiDAR modalities, offering a cost-effective and non-intrusive solution for tracking individuals. This dataset and the methodologies it supports promise to inspire further innovations in sensor fusion and biometrics, positioning Huang as a key contributor to the evolving landscape of intelligent sensing systems.
Research Focus
Key Achievements
Top Papers
- 1