Research on the remote monitoring system for falls in the elderly based on the internet of things and six axis acceleration sensors

Abstract

With the integration and collision of the Internet of Things, machine learning, Big data computing and other technologies, related applications of the Internet of Things also need to process a lot of real-time data streams. This article focuses on the research of remote monitoring of falls using the Internet of Things and six axis acceleration sensors, and explores the efficient application of Internet of Things technology in the system. The hardware design and related software development of remote monitoring of falls for the elderly are the key parts, and the overall framework, main modules, and specific implementation of the system are elaborated in detail. A complete remote monitoring system is designed by selecting a suitable six-axis acceleration sensor, collecting and analyzing the data. The continuous development of the Internet of Things and six axis acceleration sensor technology can provide real-time intelligent remote monitoring. Compared to cloud computing platforms, edge clusters have limited computing and storage resources and diverse types of computing node architectures. Therefore, it is necessary to use lightweight application service deployment methods to build an efficient and autonomous data processing platform. Through research and innovation on the remote monitoring system for elderly falls, with optimized and comprehensive technology and detailed research support, the overall system design was experimentally debugged and the experimental plan was ultimately determined. Through data communication module, fall detection and diagnosis module, and database management module, rapid analysis of remote acceleration data and information exchange are achieved, thereby minimizing the possibility of accidents caused by falls in the elderly.