卷积神经网络
基带
计算机科学
雷达
毫米波
人工智能
人工神经网络
极高频率
核(代数)
模式识别(心理学)
灵敏度(控制系统)
直线(几何图形)
电子工程
电信
工程类
带宽(计算)
数学
光学
物理
组合数学
几何学
作者
Bo Wang,Liang Guo,Hao Zhang,Yan Guo
出处
期刊:IEEE Sensors Journal
[Institute of Electrical and Electronics Engineers]
日期:2020-11-15
卷期号:20 (22): 13364-13370
被引量:39
标识
DOI:10.1109/jsen.2020.3006918
摘要
Fall accidents are significant threats to the health and life of older people. When a millimetre-wave (mmWave) frequency modulated continuous wave (FMCW) radar is used for fall detection, the selected features for further classification can determine the detection performance. In this paper, a line kernel convolutional neural network (LKCNN) is proposed to process the baseband data directly to detect fall motions. This method utilizes the characteristic of a convolutional neural network (CNN) that it can learn to extract useful features during the training process. A data sample generation method is also proposed to generate multiple samples for the training process by utilizing the multiple receiving channels and sufficiently small pulse repetition time (PRT). The experiment results show that the proposed method can detect fall motions with high accuracy, sensitivity and specificity with fewer network parameters and less computation cost, which is meaningful in realizing an all-time indoor fall detection system.
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