GRD Journals- Global Research and Development Journal for Engineering | Volume 4 | Issue 4 | March 2019 ISSN: 2455-5703
Sensor Based System to Monitor Driver’s Stress, Fatigue and Drowsiness using Data Analytic Approach S. Suma Assistant Professor Department of Computer Science & Engineering Valliammai Engineering College, Tamil Nadu, India J. Akash UG Student Department of Computer Science & Engineering Valliammai Engineering College, Tamil Nadu, India
R. V. Kamala Kannan UG Student Department of Computer Science & Engineering Valliammai Engineering College, Tamil Nadu, India R. Kishan UG Student Department of Computer Science & Engineering Valliammai Engineering College, Tamil Nadu, India
Abstract The level of stress for drivers will have a greater impact while driving. Driver stress may affect driver performance and create many accidents. Sometimes many innocent lives are lost because of driver stress level. Stress is something we cannot able to physically see and identify, hence different types of sensors like heartbeat, pupil dilation, blood pressure, respiration rate, skin response are used to predict stress level of the driver and driver behavior while driving. Driver behavior parameters like ECG, eye blinking, speed, steering angle, turn signal can be monitored while driving. These parameters might be helpful to observe driver’s distractions as well. In this project, to overcome the challenge we have proposed real data collection, IoT based sharing and data analytics. To obtain driver stress, we are planning to integrate heartbeat and eye blink sensors to predict the stress level and drowsiness of the driver during driving. In real time these sensors would be embedded in the driver glass. The real time data are processed from NetBeans as an excel file to R programming studio for data analysis. Keywords- Driver, Data Analytics, Stress, Real Data Collection, Drowsiness
I. INTRODUCTION Stress can be viewed as a physiological reaction to ordinary, passionate, mental and physical difficulties. A long-haul presentation to upsetting circumstances can have negative wellbeing results. For example, the expanded danger of cardiovascular sicknesses and insusceptible framework issue. Subsequently, an auspicious pressure location can prompt frameworks for better administration. The dimensions of pressure while driving influence the manner in which we drive and affect the probability of having a mishap. In an existing framework, there is no gadget to distinguish/anticipate driver stress and driving proficiency. Because of the absence of driver stress discovery framework, numerous mishaps/misfortunes of life occur. A piece of exploration information introduced arranges the significant hazard factors in charge of traffic mishaps as indicated by their effect as human variables (82%), vehicle factors (2.6%), street/ecological components (2.6%), and others (2.8%). Human elements are in charge of a significant number of the traffic mishaps out and about. The information introduced in ongoing exploration article orders the real hazard factors in charge of traffic mishaps as indicated by their effect as human elements (92%). Among these, drivers' human elements comprise of subjective mistakes (40.6%), judgment blunders (34.1%), execution blunders (10.3%), and others (15%). Intellectual blunders show up in very psychological requesting circumstances in which the subjective load as seen by the driver is high and the moves made by the driver to deal with those circumstances are in numerous events not fitting. Having the capacity to identify the advancement of the driver's intellectual load and feelings of anxiety and to anticipate exceptionally requesting circumstances is vital so as to give assistance to the driver to more readily deal with these circumstances. To detect the driver conditions, methods based on vision or physiological signals can be used. The vision-based method uses cameras and image processing to monitor the eyelid, head movement, and facial expression of the driver. However, sensitivity to environmental factors, such as lighting conditions is a major problem that must be solved. Furthermore, this method can detect a condition only after it begins to show on the face. The method based on physiological signals detects abnormal conditions by monitoring the change in these signals according to each condition.
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