This article presents a new deep-learning approach for automated fall detection from signatures of a continuous-wave micro-Doppler radar.
Motivated by these insights, we developed a deep-learning-based fall detection network on a Doppler radar sensor called convolutional bidirectional long short- ...
Abstract— Falls are a major public health concern and the leading cause of accidental deaths among elders. Technologies capable of fast and accurate fall ...
Aug 20, 2024 · In this paper, a deep learning model is proposed based on the frequency spectrum of radar signals, called the convolutional bidirectional long short-term ...
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Aug 26, 2024 · Extensive comparison experiments demonstrate that our model achieves an accuracy of 98.83% in detecting falls, surpassing other relevant methods ...
Aryokee is introduced, an RF-based fall detection system that uses convolutional neural networks governed by a state machine that works with new people and ...
The results demonstrate that the proposed fall detection method outperforms the other methods in terms of higher accuracy, precision, sensitivity, and.
Fall Detection on a single Doppler Radar Sensor by using Convolutional Neural Networks · Engineering, Computer Science. 2019 IEEE International Conference on ...
In [17], the authors combine convolutional neural networks (CNNs) and AEs to classify 12 actions based on micro-Doppler signatures. In [18] , two DNNs ...
We introduce Aryokee, an RF-based fall detection system that uses convolutional neural networks governed by a state machine.
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