Fall Detection System Based on Point Cloud Enhancement Model for 24 GHz FMCW Radar

Autor: Tingxuan Liang, Ruizhi Liu, Lei Yang, Yue Lin, C.-J. Richard Shi, Hongtao Xu
Jazyk: angličtina
Rok vydání: 2024
Předmět:
Zdroj: Sensors, Vol 24, Iss 2, p 648 (2024)
Druh dokumentu: article
ISSN: 24020648
1424-8220
DOI: 10.3390/s24020648
Popis: Automatic fall detection plays a significant role in monitoring the health of senior citizens. In particular, millimeter-wave radar sensors are relevant for human pose recognition in an indoor environment due to their advantages of privacy protection, low hardware cost, and wide range of working conditions. However, low-quality point clouds from 4D radar diminish the reliability of fall detection. To improve the detection accuracy, conventional methods utilize more costly hardware. In this study, we propose a model that can provide high-quality three-dimensional point cloud images of the human body at a low cost. To improve the accuracy and effectiveness of fall detection, a system that extracts distribution features through small radar antenna arrays is developed. The proposed system achieved 99.1% and 98.9% accuracy on test datasets pertaining to new subjects and new environments, respectively.
Databáze: Directory of Open Access Journals
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