Parallel Neural Network–Convolutional Neural Networks for Wearable Motorcycle Airbag System

Abstract

Recently, motorcycle accidents have increased as the number of motorcycle drivers has increased. Although the head and neck are the body parts most frequently injured when a motorcycle accident occurs, there is a lack of research on the protection afforded to the neck by the safety equipment used by motorcycle drivers. This study presents an airbag system that uses artificial intelligence to prevent injury to the neck of a motorcycle driver. It uses a six-axis sensor, the MPU6050 sensor, which measures acceleration and angular velocity in real time as the user moves. The angles are obtained by using the measured acceleration and angular velocity, and the accident situation is judged by AI, which analyzes the acceleration and angle data. Because data is needed for AI to learn, data by type were collected through experiments. In this study, we compare the judgement performance of a parallel neural networks–convolutional neural network and a parallel neural network.