Analysis of Automobile Drivers Stress By Using Physiological Signals

This paper gives an analysis of variation of the physiological signals of a person with respect to the stress developed within him/her. Normally automobile drivers feel tired(more stress) during long travel. Many accidents occur due to the tiredness of the driver. Due to over tired, the driver may sleep during driving. To overcome this difficulties, we propose a new concept using Electro Encyclogram Signal(EEG) of the driver. The analysis was done using EEG to get different stress levels. The EEG for normal persons and abnormal persons and stressed persons are stored in a database. The EEG of the driver is frequently monitored and supposed for feature extraction. This process is done using Machine learning algorithm. The feature is compared with the existing data using Artificial Neural Networks(ANN). The result shows whether the driver is normal or in tired. The analysis points out the fact that the physiological signals can be used as a metric for monitoring the stress of a person. Keywords—Stress, QRS power, ECG, breathing rate, respiratory signal, correlation coefficient. I . INTRODUCTION American Psychological Association defines stress as ―any uncomfortable emotional experience accompanied by predictable biochemical, physiological and behavioral changes‖. Reaction of a person from a normal state to an exited state in order to preserve his/her integrity can also be defined as stress. An extreme amount of stress can produce extreme health issues and it can affect the cardiovascular and central nervous system. Modern automobiles incorporate an intelligent system to monitor the stress level of the driver inside it to ensure the safety[6]. According to the US census bureau, 10% of the total road accidents in 2005 were caused due to the distraction of the drivers during driving. The number rose to 16% in 2009. Another statistics given by the Network of Employers for Traffic Safety (NETS) say that 2050% of the accidents are caused due driver’s distraction. These statistical results show the importance of analysis of stress in a person. Stressful mind does not produce a constructive result. In Earlier days analysis of the stress is done using the ECG and the respiratory signals acquired from the automobile drivers. Stress of an automobile driver is highly correlated to heart rate and Galvanic Skin Response (GSR) [2]. We intend to analyze the stress in a different way using different feature signals extracted from the ECG and the respiratory signal. This study can pave the way for more options of physiological signal features that reflect the stress of a person. It is proved that the heart rate and the galvanic skin resistance are the two features that are highly correlated with the stress of a person. Using this result, the analysis was done in this paper by taking heart rate as the marker signal. In previous method QRS power was the feature signal extracted from the ECG signal of the driver [1]. This feature signal was further correlated with the marker signal. It was able to see a clear variation in the correlation when the stress varies. Breathing rate was the feature signal extracted from the respiratory signals. This was also correlated with the stress marker signal. Both feature signals showed a direct relation with the stress marker signal. The analysis is finally tabulated to check the change in the correlation coefficients of the feature signals. The analysis Jennifer A. Healy, Rosalind W. Picard et al in their paper of Detection Stress During Real-World Driving Tasks Using Physiological Sensors suggested that methods for collecting and analyzing physiological data during real world driving tasks to determine a driver's relative stress level. Electrocardiogram, electromyogram, skin conductance and respiration were recorded RESEARCH ARTICLE OPEN ACCESS International Journal of Engineering Research and Applications (IJERA) ISSN: 2248-9622 International Conference on Humming Bird ( 01st March 2014) Cape Institute of Technology 51 | P a g e continuously while drivers followed a set route through open roads in the greater Boston area[2]. Data from twenty-four drives of at least fifty minute duration were collected for analysis. The data were analyzed in two ways. Analysis I used features from five minute intervals of data during the rest, highway and city driving conditions to distinguish three levels of driver stress with an accuracy of over 97% across multiple drivers and driving days. Analysis II compared continuous features, calculated at one second intervals throughout the entire drive, with a metric of observable stressors created by independent coders from video tapes. The results show that for most drivers studied, skin conductivity and heart rate metrics are most closely correlated with driver stress level. These recordings indicate that physiological signals can provide a metric of driver stress in future cars capable of physiological monitoring. Such a metric could be used to help manage non-critical in-vehicle Information systems and could also provide a continuous measure of how different road and traffic conditions affect drivers. This experiment was designed to monitor drivers' physiologic reactions during real-world driving. II. PHYSIOLOGICAL SIGNALS FEATURE