Fall Detection Based on Continuous Wave Radar Sensor Using Binarized Neural Networks

Abstract

Accidents caused by falls among the elderly have become a significant social issue, making fall detection systems increasingly needed. Fall detection systems such as internet of things (IoT) devices must be affordable and compact because they must be installed in various locations around the house, such as bedrooms, living rooms, and bathrooms. In this study, we propose a lightweight fall detection method using a continuous-wave (CW) radar sensor and a binarized neural network (BNN) to meet these requirements. We used a CW radar sensor, which is more affordable than other types of radar sensors, and employed a BNN with binarized features and parameters to reduce memory usage and make the system lighter. The proposed method distinguishes movements using micro-Doppler signatures, and spectrogram is binarized as an input to the BNN. The proposed method achieved 93.1% accuracy in binary classification of five fall actions and six non-fall actions. The memory requirements for storing parameters were reduced to 11.9 KB, representing a reduction of up to 99.9% compared with previous studies.

Keywords

• fall detection
• internet of things
• lightweight
• continuous wave radar
• short-time Fourier transform
• micro-Doppler signature