Automatic fall detection by using Doppler-radar and LSTM-based recurrent neural network
- Resource Type
- Conference
- Authors
- Imamura, Takayuki; Moshnyaga, Vasily G.; Hashimoto, Koji
- Source
- 2022 IEEE 4th Global Conference on Life Sciences and Technologies (LifeTech) Life Sciences and Technologies (LifeTech), 2022 IEEE 4th Global Conference on. :36-37 Mar, 2022
- Subject
- Bioengineering
Computing and Processing
General Topics for Engineers
Robotics and Control Systems
Signal Processing and Analysis
Time-frequency analysis
Recurrent neural networks
Radar detection
Stairs
Real-time systems
Life sciences
Fall detection
assistive technology
fall detection
machine learning
RNN
Long Short-Term Memory
Doppler radar
- Language
Falls are a major public health concern among seniors living independently at home. This article presents a new approach for automated real-time fall detection by a single continuous-wave micro-Doppler radar and deep learning. Unlike related methods, we apply Long Short-Term Memory based recurrent neural network (RNN) to time-frequency spectrograms of Doppler-radar returns to identify falls. The approach does not require extra hardware or big data set to classify falls from non-fall actions and has superior accuracy compared to CNN-based fall detection.