A Millimetre-Wave Radar-Based Fall Detection Method Using Line Kernel Convolutional Neural Network

• Published in: IEEE sensors journal 2020-11, Vol.20 (22), p.13364-13370
• Main Authors: Wang, Bo, Guo, Liang, Zhang, Hao, Guo, Yong-Xin
• Format: Article
• Language: English
• Subjects: Artificial neural networks; Baseband; Chirp; Continuous radiation; Convolutional neural network; data sample generation; Fall detection; Feature extraction; Kernels; line convolution kernel; Millimeter waves; millimetre-wave radar; Neural networks; Parameter sensitivity; Radar; Radar antennas; Radar detection; Sensors; Training
• ISSN: 1530-437X; 1558-1748
• DOI: 10.1109/JSEN.2020.3006918

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.