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A Low Power Fall Sensing Technology Based on FD-CNN

The Bluetooth-based wearable fall detection technology faces problems such as short transmission distance, easily interfered by obstacles, and high-power consumption. To address these issues, we developed a sensor board integrated with low-power ZigBee and MPU6050, which can sample and cache three-axial acceleration and angular velocity data in the sleep mode; we also designed an interrupt-driven algorithm that can collect and transmit the data to the receiving end (namely server) with low-power consumption via ZigBee. Additionally, the received data are normalized according to the range specification and cached into a sliding window by the server. Meanwhile, the cached data are mapped into RGB bitmap, and a fall detection convolutional neural network (FD-CNN) is designed and trained using the open dataset to identify falls from the activities of daily livings according to bitmap. The experimental results show that the average accuracy of this method is 98.61%, while its average sensitivity and specificity are 98.62% and 99.80%, respectively. It takes advantage of the strong networking capacity of ZigBee, and the strong computing power of the server, which is very suitable for fall sensing in elderly community with low power and high accuracy.