Driver drowsiness detection using different classification algorithms

Capability of electrocardiogram (ECG) signal in contributing to the daily application keeps developing days by days. As technology advances, ECG marks the possibility as a potential mechanism towards the drowsiness detection system. Driver drowsiness is a state between sleeping and being awake due to body fatigue while driving. This condition has become a common issue that leads to road accidents and death. It is proven in previous studies that biological signals are closely related to a person's reaction. Electrocardiogram (ECG) is an electrical indicator of the heart, provides such criteria as it reflects the heart activity that can detect changes in human response which relates to our emotions and reactions. Thus, this study proposed a non-intrusive detector to detect driver drowsiness by using the ECG. This study obtained ECG data from the ULg multimodality drowsiness database to simulate the different stages of sleep, which are PVT1 as early sleep while PVT2 as deep sleep. The signals are later processed in MATLAB using Savitzky-Golay filter to remove artifacts in the signal. Then, QRS complexes are extracted from the acquired ECG signal. The process was followed by classifying the ECG signal using Machine Learning (ML) tools. The classification techniques that include Multilayer Perceptron (MLP), k-Nearest Neighbour (IBk) and Bayes Network (BN) algorithms proved to support the argument made in both PVT1 and PVT2 to measure the accuracy of the data acquired. As a result, PVT1 and PVT2 are correctly classified as the result shown with higher percentage accuracy on each PVTs. Hence, this paper present and prove the reliability of ECG signal for drowsiness detection in classifying high accuracy ECG data using different classification algorithms

Electrocardiogram (ECG) based stress recognition integrated with different classification of age and gender

Good mental health is important in our daily life. A person commonly finds stress as a barrier to enhance an individual’s performance. Be reminded that not all people have the same level of stress because different people have dissimilar problems in their life. In addition, different level of age and gender will affect unequal amount of stress. Electrocardiogram (ECG) signal is an electrical indicator of the heart that can detect changes of human response which relates to our emotions and reactions. Thus, this research proposed a non-intrusive detector to identify stress level for both gender and different classification of age using the ECG. A total of 30 healthy subjects were involved during the data acquisition stage. Data acquisition which initialize ECG data were divided into two conditions; which are normal and stress states. ECG data for normal state only need the participant to breath in and out normally. In other hand, the participants also need to undergo Stroop Colour word test as a stress inducer to represent ECG in stress state. Then, Sgolay filter was selected in the pre-processing stage to remove artifacts in the signal. The process was followed by feature extraction of the ECG signal and finally classified using RR Interval (RRI), different amplitudes of R peaks and Cardioid graph were used to evaluate the performance of the proposed technique. As a result, Class 5 (age range between 50-59 years old) marks the highest changes of stress level rather than other classes, while women are more affected by stress rather than men by showing tremendous percentage changes between normal and stress level over the proposed classifiers. The result proves that ECG signals can be used as an alternative mechanism to recognize stress more efficiently with the integration of gender and age variabilities

A review of ECG data acquisition for driver drowsiness detection

Over the years, cases related to road accidents and road fatalities keeps increasing. Both cases have potential to put life of a person at risk. One of the factors that leads to accidents are drowsiness. However, several lives can be saved with accurate and reliable drowsiness detection system. Thus, many researchers take this issue seriously by developing drowsiness detection mechanism in reducing cases related to driver drowsiness. As drowsiness is strongly correlated with the heart activities,hence bio-signal is the most preferable indicator to measure the drowsiness level. Reflection of electrical signal in the human body known as Electrocardiogram (ECG) are widely used in monitoring human action and reaction to prevent the occurrence of these devastating incidents. Thus, this paper will review the drowsiness detection technique focusing in ECG data acquisition for driver drowsiness detection. As the first step plays an important role for the whole system, this paper discussed on some open issues in drowsiness mechanism. We hope that this review will support and give some ideas to the future researchers in increasing the reliability of ECG measures towards driver drowsiness detection in reducing accident cases

Development of a driver drowsiness monitoring system using electrocardiogram

Driver drowsiness has become a common issue that leads to road accidents and death. Accidents not only affect the physical body of the driver, but it also affects people in the surrounding, physical road conditions, and environments. It is proven in previous studies that biological signal are closely related to a person’s reaction. Electrocardiogram (ECG), which is an electrical indicator of the heart, provides such criteria as it reflects the heart activity. Morphological signal of the heart is strongly correlated to our actions which relates to our emotions and reactions. Thus, this study proposed a non-intrusive detector to detect driver drowsiness by using the ECG. A total of 10 subjects were obtained from The Cyclic Alternating Pattern (CAP) Sleep database. The signals are later processed using low pass Butterworth filter with 0.1 cutoff frequency. Then, QRS complexes are extracted from the acquired ECG signal. Classification techniques such as RR interval and different of amplitude at R peak were used in order to differentiate between normal and drowsy ECG signal. Cardioid based graph was used to support the argument made in analyzing area and circumference of both normal and drowsy graph. The result shows that RR Interval of a drowsy state increased almost 22% rather than in normal state. The percentage different of amplitude difference at R peak between normal and drowsy state can reach up to 36.33%. In terms of cardioid, area, perimeter and Euclidean distance of the centroid are always higher than drowsy. Thus, from the outcomes that been suggested for drowsiness detection using RR interval and amplitude of R are able to become as the most efficient drowsiness detection

Enhancing driver drowsiness detection for data acquisition stage using electrocardiogram

Road accidents can occur based on many factors and one of them is due to driver drowsiness. These fatalities could cause death which affects our country’s economy. Thus, this study proposed a driver drowsiness detection based on Electrocardiogram (ECG) for the data acquisition stage. ECG has been used in collecting data from the human body that used electrodes and place it on human skin to detect the electrical activity of the heart. This study proposed a drowsiness detection through ECG signal involving 10 subjects aged in their early 20s regardless of their gender. All subject used for this test is free from any kind of drugs, alcohol or even caffeine. The ECG data were collected from a source called The ULG Multimodality Drowsiness Database (DROZY). Next, the signal obtains from the database does not need to undergo the filtering process since the R-peak of the data can easily be detected. The feature that has been extracted is the R peak so the HRV analysis can be used to classify the state of the subject, either awake or drowsy. Other than that, the data of the cardioid of each subject also being measured and the Euclidean distance of it being compared. The outcome of this study shows that the amplitude of the drowsy phase will be lower compared to the normal state and the same goes for the Euclidean distance of Cardioid based graph