Fall detection with a single Doppler radar sensor and LSTM recurrent neural network
- Resource Type
- Conference
- Authors
- Imamura, Takayuki; Moshnyaga, Vasily G.; Hashimoto, Koji
- Source
- 2022 IEEE 65th International Midwest Symposium on Circuits and Systems (MWSCAS) Circuits and Systems (MWSCAS), 2022 IEEE 65th International Midwest Symposium on. :1-4 Aug, 2022
- Subject
- Communication, Networking and Broadcast Technologies
Components, Circuits, Devices and Systems
Robotics and Control Systems
Signal Processing and Analysis
Time-frequency analysis
Recurrent neural networks
Shape
System performance
Stairs
Real-time systems
Doppler radar
sensor
machine learning
Long Short-Term Memory
RNN
fall detection
- Language
- ISSN
- 1558-3899
Falls are a serious health concern and a main cause of injuries among elders living independently at home. In this paper, we describe a new system for real-time automatic fall detection. In contrast to related formulations, the system employs a single continuous-wave micro-Doppler radar sensor to monitor a subject and Long Short-Term Memory based recurrent neural network (RNN) to identify falls from the time-frequency characteristics of the sensor’s returns. It does not require extra hardware or big data set to classify abrupt and slow falls from non-fall actions with superior detection accuracy.