A Study on Wearable Airbag System Applied with Convolutional Neural Networks for Safety of Motorcycle

Injuries to the head and the neck are the most frequent in the event of motorcycle accidents. But enough research has not been done to protect the neck. This paper presents an airbag system that recognizes the accident situation with Artificial Intelligence to protect the driver's neck area from motorcycle accident situations when driving. In some papers with similar themes, most of them are judged based on a critical point. However, in the case of an accident judgment using the critical point, a malfunction may occur such that the airbag operates when a similar operation is performed, or the airbag does not operate due to failing to pass the critical point at the time of an accident. Artificial intelligence was used to avoid malfunctions and inconveniences. Artificial intelligence can solve the problem of malfunction that occurs when it is judged as a critical point and can solve the inconvenience of commercialized products. The CNN presented in this paper can solve these two problems, and the accuracy of accident judgment is as high as 95.75%. Through the MPU 6050 sensor, it operates the airbag by determining the accident situation using the Artificial Intelligence that was learned in advance through the information on acceleration and angular velocity of the driver's movements that were measured in real time. To make Artificial Intelligence learn, the data were collected by dividing several types of accidents on motorcycles. In this paper, the Artificial Intelligence made by Convolutional Neural Networks (CNN) method and the Artificial Intelligence made by Neural Networks (NN) method is compared, and it is confirmed that the performance such as Test Accuracy or Train Accuracy of CNN is better.