RF-Based Fall Monitoring Using Convolutional Neural Networks

Yonglong Tian*      Guang-He Lee*      Hao He*      Chen-Yu Hsu      Dina Katabi

Massachusetts Institute of Technology


We introduce Aryokee, an RF-based fall detection system that uses convolutional neural networks governed by a state machine. Aryokee works with new people and environments unseen in the training set. It also separates different sources of motion to increase robustness. Results from testing Aryokee with over 140 people performing 40 types of activities in 57 different environments show a recall of 94% and a precision of 92% in detecting falls.



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