Multi-sensor Information Fusion for Classification of Driver's Physiological Sensor Data

Physiological sensor signals analysis is common practice in medical domain for diagnosis and classification of various physiological conditions. Clinicians' frequently use physiological sensor signals to diagnose individual's psychophysiological parameters i.e., stress tiredness, and fatigue etc. However, parameters obtained from physiological sensors could vary because of individual's age, gender, physical conditions etc. and analyzing data from a single sensor could mislead the diagnosis result. Today, one proposition is that sensor signal fusion can provide more reliable and efficient outcome than using data from single sensor and it is also becoming significant in numerous diagnosis fields including medical diagnosis and classification. Case-Based Reasoning (CBR) is another well established and recognized method in health sciences. Here, an entropy based algorithm, " Multivariate Multiscale Entropy analysis " has been selected to fuse multiple sensor signals. Other physiological sensor signals measurements are also taken into consideration for system evaluation. A CBR system is proposed to classify 'healthy' and 'stressed' persons using both fused features and other physiological i.e. Heart Rate Variability (HRV), Respiratory Sinus Arrhythmia (RSA), Finger Temperature (FT) features. The evaluation and performance analysis of the system have been done and the results of the classification based on data fusion and physiological measurements are presented in this thesis work.