Paper
11 September 2024 Human fall detection method with millimeter-wave radar based on improved point transformer
Kewang Zhou, Junsheng Xiao, Zhichun Wang, Xiaofeng Zhou
Author Affiliations +
Proceedings Volume 13253, Fourth International Conference on Signal Image Processing and Communication (ICSIPC 2024); 132531N (2024) https://doi.org/10.1117/12.3041472
Event: 4th International Conference on Signal Image Processing and Communication, 2024, Xi'an, China
Abstract
Addressing the problem that existing point cloud network models do not make sufficient use of millimetre-wave radar point cloud information, a millimetre-wave radar human fall detection method based on an improved point transformer is proposed. The method uses the improved point transformer network model to extract the multidimensional feature information of the point cloud for classification and identification to achieve accurate fall detection. Several human action data from different environments and different individuals were collected to construct a human posture point cloud dataset. Comparative experiments show that the proposed millimeter-wave radar human fall detection method based on the improved point transformer makes full use of the millimeter-wave radar point cloud information, achieves up to 99.37% detection accuracy, and shows good generalisation ability.
(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.
Kewang Zhou, Junsheng Xiao, Zhichun Wang, and Xiaofeng Zhou "Human fall detection method with millimeter-wave radar based on improved point transformer", Proc. SPIE 13253, Fourth International Conference on Signal Image Processing and Communication (ICSIPC 2024), 132531N (11 September 2024); https://doi.org/10.1117/12.3041472
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KEYWORDS
Point clouds

Transformers

Radar sensor technology

Feature extraction

Data modeling

Radar

Matrices

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