mmFall: Fall Detection using 4D mmWave Radar and a Hybrid Variational RNN AutoEncoder
F. Jin, A. Sengupta, S. Cao
IEEE Transactions on Automation Science and Engineering, 2022 , Vol. 19 (2) , pp. 1245-1257
Abstract
mmFall presents a hybrid variational RNN autoencoder architecture for fall detection using 4D mmWave radar. The system analyzes micro-Doppler and range-Doppler features with temporal modeling through RNN variants, achieving robust real-time fall detection while maintaining privacy. Achieves 98% detection rate out of 50 falls with only 2 false alarms.
Citation
F. Jin, A. Sengupta, S. Cao. "mmFall: Fall Detection using 4D mmWave Radar and a Hybrid Variational RNN AutoEncoder." IEEE Transactions on Automation Science and Engineering 19(2): 1245-1257, 2022. DOI: 10.1109/TASE.2020.3042158
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Details
- Year
- 2022
- Published In
- IEEE Transactions on Automation Science and Engineering
- Volume
- 19
- Issue
- 2
- Pages
- 1245-1257
- DOI
- 10.1109/TASE.2020.3042158