Besides the technical environment the safety and efficacy of modern vehicular traffic depends very much on the human factors. These factors include mental state, health, mood, personality, tiredness, inadvertence and the momentary ability of decision taking of the vehicle driver.
For several years the Medical Informatics Laboratory at the Faculty of Control Engineering and Information Technology has conducted research in order to develop processing and analyzing algorithms to determine parameters of safe vehicle driving. As a part of the project this work aims to discuss the specification, design and implementation steps of a monitoring application developed as part of my thesis work.
The main goal of the application is to interconnect with the lab's formerly developed Bluetooth ECG device and to record, plot, save and process the ECG signals sent by it in order to determine the vehicle drivers stress and tiredness factor.
To achieve this, we first need to filter the recorded signals and separate the two different Einthoven channels. After separating the channels we attempt to recognize the QRS complexes by applying an algorithm based on a special mixture of the amplitude and sample fitting methods. Having found the QRS complexes, NN intervals can easily be calculated, thus obtaining the DES series, which represents the distance between two adjacent R peaks as a function of time. Finally the stress factor estimation is done by means of analyzing the proportion of low- and high frequency components in the power spectral density of the DES function. This decision making method was inspected and found to be proper on several measurements.
The application stores the patient data in a small embedded database, by automatically generating the relevant records for of all the measurements and connecting them to the selected vehicle driver.
Summing up we can say that as a result of this thesis work a monitoring application has been implemented which is able to give an acceptable estimation of a vehicle driver's momentary stress and tiredness factor, thus warning the driver that he/she might not be in proper condition for a safe drive.