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| Citation: | HE Mi, PING Qinwen, and DAI Ran. Fall detection based on deep learning fusing ultrawideband radar spectrograms[J]. Journal of Radars, 2023, 12(2): 343–355. doi: 10.12000/JR22169
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Fall Detection Based on Deep Learning Fusing Ultrawideband Radar Spectrograms
doi: 10.12000/JR22169
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Abstract
Compared with narrowband Doppler radar, ultrawideband radar can simultaneously acquire the range and Doppler information of targets, which is more beneficial for behavior recognition. To improve the recognition performance of fall behavior, frequency-modulated continuous-wave ultrawideband (UWB) radar was applied to collect daily behavior and fall data of 36 subjects in two real indoor complex scenes, and a multi-scene fall detection dataset was established with various action types; the range-time, time-Doppler, and range-Doppler spectrograms of the subjects were obtained after preprocessing radar data; based on the MobileNet-V3 lightweight network, three types of deep learning fusion networks at the data level, feature level, and decision level were designed for the radar spectrograms, respectively. A statistical analysis shows that the decision level fusion method proposed in this paper can improve fall detection performance compared with those using one type of spectrogram, the data level and the feature level fusion methods (all P values by significance test method are less than 0.003). The accuracies of 5-fold cross-validation and testing in the new scene of the decision level fusion method are 0.9956 and 0.9778, respectively, which indicates the good generalization ability of the proposed method. -
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References
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- Figure 1. Overall research block diagram of deep learning fusing ultrawideband radar spectrograms for fall detection
- Figure 2. Schematic diagram of transmitting and receiving waveforms of the FMCW radar
- Figure 3. Arrangement diagram of range-time matrix RT
- Figure 4. Range-time spectrograms of fall and walk
- Figure 5. Arrangement diagram of range-Doppler matrix RD
- Figure 6. Range-Doppler spectrograms of fall and walk
- Figure 7. Time-Doppler spectrograms of fall and walk
- Figure 8. Core structure of MobileNet-V3
- Figure 9. Structure diagram of data level fusion network
- Figure 10. Structure diagram of feature level fusion network
- Figure 11. Structure diagram of decision level fusion network
- Figure 12. K band UWB radar fall detection system
- Figure 13. Schematic diagram of daily behaviors and a part of falls designed in the experiment
- Figure 14. Multi-scene schematic diagram of the experiment
- Figure 15. Curves of accuracy and cross entropy loss of 5-fold cross-validation using one kind of spectrograms for fall detection
- Figure 16. Curves of accuracy and cross entropy loss of 5-fold cross-validation using fusion methods for fall detection
- Figure 17. Confusion matrix obtained when testing various models using data of Scene 2
- Figure 1. Release webpage of K band UWB radar spectrogram dataset-1.0 for fall detection