Drowsy driving detection using neural network with backpropagation algorithm implemented by FPGA

摘要

Neural networks have recently attracted much attention due to the development of artificial intelligence or deep learning technology. These can be implemented by applying the current hardware technology such as a central processing unit and a graphics processing unit. In this case, the applications are limited because considerable power and large volume are used. To overcome these shortcomings, hardware development for artificial intelligence is accelerated, and this technology is called the neuromorphic system, which is especially suitable for low-power and small-area applications such as wearable devices. In this study, the neuromorphic system is implemented using the field-programmable gate array (FPGA), and it is applied to wearable systems. This system is especially developed for a module that measures the drowsiness of a user based on biosignals such as electrocardiogram (ECG) and electromyography (EMG). The measured biosignals are fed to the neuromorphic system for supervised learning using the backpropagation algorithm. Therefore, it is possible to make the drowsiness driving assessment specific to each user, and the error on the user's condition can be minimized. In addition, by integrating artificial intelligence including learning algorithm and biosensor circuits, it is possible to minimize disturbance to the driver or user through miniaturization and low power consumption.