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https://scholarbank.nus.edu.sg/handle/10635/187350
Title: | A Millimetre-wave Radar Based Fall Detection Method using Line Kernel Convolutional Neural Network | Authors: | WANG BO ZHANG HAO GUO LIANG GUO YONGXIN |
Keywords: | Convolutional neural network Data sample generation Fall Detection Line convolution kernel Millimetre-wave radar |
Issue Date: | 5-Nov-2020 | Citation: | WANG BO, ZHANG HAO, GUO LIANG, GUO YONGXIN (2020-11-05). A Millimetre-wave Radar Based Fall Detection Method using Line Kernel Convolutional Neural Network. IEEE Sensors Journal 20 (22) : 13364-13370. ScholarBank@NUS Repository. | Abstract: | 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. © 2001-2012 IEEE. | Source Title: | IEEE Sensors Journal | URI: | https://scholarbank.nus.edu.sg/handle/10635/187350 | ISSN: | 1530437X 15581748 |
Appears in Collections: | Students Publications Staff Publications Elements |
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