Accident Detection System for Bicycle Riders

Abstract

Bicycle riders are exposed to accident injuries such as head trauma. The risk of these riders’ injuries is higher compared to the risk of injuries for motorists. Crashes, riders’ errors, and environmental hazards are the cause of bicycle-related accidents. In 2017, nearly 50% of bicycle-related accidents occurred in urban areas at night, which may contribute to a delay in reporting the accidents to emergency centers. Hence, a system that can detect the accident is needed to notify urgent care clinics promptly. In this article, we propose a bicycle accident detection system. We designed hardware modules measuring the features related to the riding status of a bicycle and fall accidents. For this purpose, we used a magnetic, angular rate, and gravity (MARG) sensor-based system which measures four different types of signals: 1) acceleration, 2) angular velocity, 3) angle, and 4) magnitude of the riding status. Each of these signals is measured in three different directions (<inline-formula> <tex-math notation= LaTeX >${X}$ </tex-math></inline-formula>, <inline-formula> <tex-math notation= LaTeX >${Y}$ </tex-math></inline-formula>, and <inline-formula> <tex-math notation= LaTeX >${Z}$ </tex-math></inline-formula>). We used two different time-domain parameters, i.e., average and standard deviation. As a result, we considered 24 features. We used principal component analysis (PCA) for feature reduction and the support vector machines (SVM) algorithm for the detection of fall accidents. Experimental results show that our proposed system detects fall accidents during cycling status with 95.2% accuracy, which demonstrates the feasibility of our proposed bicycle accident detection system.