Paper
23 August 2023 The Linux based body falls alarm system
Shuai Chen, Zhian Zhang, Xuegong Huang
Author Affiliations +
Proceedings Volume 12784, Second International Conference on Applied Statistics, Computational Mathematics, and Software Engineering (ASCMSE 2023); 1278434 (2023) https://doi.org/10.1117/12.2692467
Event: 2023 2nd International Conference on Applied Statistics, Computational Mathematics and Software Engineering (ASCMSE 2023), 2023, Kaifeng, China
Abstract
With the development of the Internet of Things, timely alarm and real-time monitoring of the Internet of Things have become a reality and become more and more important. Traditional remote monitoring technology of the Internet of Things has been unable to meet the rapid demand for the development of modern Internet of Things industry. In order to meet the needs of the new era, real-time, secure, intelligent and extensible monitoring terminals have become a research hotspot in the modern Internet of Things industry. This paper proposes a human fall alarm system, including the establishment of arm hardware platform, the implantation of Linux system, the development of drivers and applications, the training of deep learning model, the deployment and optimization of application programs. The human falling alarm system has high practical value in the practical application, which can realize the real-time alarm and monitoring of human falling, and effectively improve the safety and convenience of home, office and other places. At the same time, the system also has a strong scalability, according to the actual demand for flexible customization and expansion.
© (2023) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Shuai Chen, Zhian Zhang, and Xuegong Huang "The Linux based body falls alarm system", Proc. SPIE 12784, Second International Conference on Applied Statistics, Computational Mathematics, and Software Engineering (ASCMSE 2023), 1278434 (23 August 2023); https://doi.org/10.1117/12.2692467
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KEYWORDS
Cameras

Education and training

Human-machine interfaces

Image processing

Detection and tracking algorithms

Safety

Deep learning

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