Drowsiness Detection Based on the Analysis of Breathing Rate Obtained from Real-time Image Recognition

Abstract

Road accidents and victims provoked by drowsiness are a worldwide relevant social and economic problem. In EU, 25% of road accidents are related with fatigue. The relation between drowsiness and accidents is supported by scientific evidences that relate microsleep and other fatigue episodes with the loss of control of the vehicle. Nowadays there exist different technological approaches for drowsiness mitigating while driving, which try to reduce these accidents by detecting driver's physical condition and acting on the driver response. The techniques for mitigating accidents are based on algorithms of detection of physiological parameters that ensure drivers confidence and an appropriate use of the technology. The most frequently used non-invasive system are the cameras, which have been explored and used, mainly detecting eye movement and eyelid closure (PERCLOS). These systems have limitations due to artifacts and noise related to environmental and emotional conditions, which might lead to false positives. With the purpose of solving these shortcomings, the goal of the research is to develop a system capable of detecting the level of drowsiness based on the involuntary movements of the driver provoked by the respiration captured by means of cameras. In the current research, robustness in front of different types of users and circumstances has been explored. A system of reduced size automotive cameras of high dynamics is proposed to be used as breathing rate sensor. Images captured by these sensors will be processed to obtain the driver's chest/abdomen movement. These data will be analyzed in real time by a validated algorithm that interprets the movement and obtains the level of fatigue and drowsiness of the driver. A twofold “gold standard” is used to compare the robustness of the developed system. An experimental design has been developed in order to take into consideration different anthropometric characteristics, clothing types, user and vehicle movement and light conditions. The result will provide the boundary conditions of any system based on on-board cameras. These data will be used for building the algorithms to detect and interpret breathing patterns. The results show the capabilities of this approach and also permit to define the needs and requirements of the resulting technological developments.