基于毫米波雷达和卷积神经网络的fpga跌倒检测系统

Jun Cao, An Feng, Bo Wang
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引用次数: 1

摘要

跌倒已成为老年人意外伤害死亡的第二大原因。及时发现老年人跌倒可以避免更大的伤害。因此,对跌落检测系统的研究变得越来越重要。在此背景下,本文设计了一个跌倒检测系统。一个跌倒检测系统通常分为两个部分:数据采集和特征识别。由于毫米波雷达具有良好的保密性和适用性,本文采用毫米波雷达进行数据采集。通常,毫米波雷达采集的数据在计算机中进行处理,并给出是否跌落的反馈。但是计算机中的数据处理软件在给系统带来极大便利的同时,也限制了系统的稳定性和可移植性,阻碍了系统的实际应用。为此,系统引入FPGA (Field Programmable Gate Array,现场可编程门阵列)来代替计算机进行所有数据处理,包括短时傅里叶变换和卷积神经网络特征识别。实验结果表明,该系统可以在简单的情况下准确检测跌倒,初步证明了FPGA在跌倒检测系统中应用的可行性。基于fpga的跌倒检测系统经过进一步的改进,可以通过带出实现小型化和低价格,促进了跌倒检测系统的实际应用。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
An FPGA-based Fall Detection System Using Millimeter-wave Radar and Convolutional Neural Network
Falls have become the second leading cause of unintentional injury death in the elderly. Timely detection of falls in the elderly can avoid greater injuries. Therefore, it is increasingly important to study fall detection systems. Based on this background, a fall detection system is designed in this paper. A fall detection system is usually divided into two parts: data acquisition and feature recognition. Due to its excellent confidentiality and applicability, millimeter-wave radar is used for data acquisition in this paper. Usually, the data collected by millimeter-wave radar is processed in a computer, and feedback is given to fall or not. But the data processing software in the computer brings great convenience to the system at the same time, it also limits the stability and portability of the system and hinders the practical application of the system. For this, the system introduces FPGA (Field Programmable Gate Array) to replace the computer for all the data processing including short-time Fourier transform and CNN (Convolutional Neural Network) feature recognition. Experimental results show that the system can detect falls accurately in simple cases, which preliminarily proved the feasibility of FPGA application in the fall detection system. The FPGA-based fall detection system can realize miniaturization and low price through tape-out after further improvement, and promote the practical application of the fall detection system.
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