Key result
A 24-GHz radar sensor and CNN achieve ~96% accuracy detecting falls, walking, and vacant states.
A 24-GHz radar sensor combined with a CNN algorithm on an Arduino Nano 33 BLE Sense can detect falls with 96.30% testing accuracy.
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May support non-contact fall monitoring development; leaves open prospective validation before clinical adoption.
In this paper, we propose a development of a smart home system for assisting elderly people by implementing a fall detection using 24-GHz radar sensor. The system is mounted on the ceiling of the room and the system records the doppler frequency shift signals of a body moving with a duration of 2.5 seconds. The recorded signals are transformed into spectrogram images with a size of 119 x 65 pixels. Then the spectrogram images are recognized by the Convolutional Neural Network (CNN) algorithm. There are 3 classes to be recognized, namely: falling, vacant, and walking. The dataset was acquired, built, and trained by using Edge Impulse platform. The hardware used in the deployment phase is Arduino Nano 33 BLE Sense. The experimental results show an accuracy rate of the training phase and the testing phase of 98.1% and 96.30%, respectively.
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Amran et al. (2022) studied Fall detection for elderly. 24-GHz radar sensor with Convolutional Neural Network (CNN) was evaluated on Accuracy rate of the testing phase. A 24-GHz radar sensor combined with a Convolutional Neural Network algorithm achieved a testing accuracy of 96.30% for detecting falls, walking, and vacant states.
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