In recent years, driver drowsiness has been one of the major causes of road accidents and can lead to severe physical injuries, deaths and significant economic losses. Statistics indicate the need of a reliable driver drowsiness detection system which could alert the driver before a mishap happens. Researchers have attempted to determine driver drowsiness using the following measures: (1) vehicle-based measures; (2) behavioral measures and (3) physiological measures. A detailed review on these measures will provide insight on the present systems, issues associated with them and the enhancements that need to be done to make a robust system. In this paper, we review these three measures as to the sensors used and discuss the advantages and limitations of each. The various ways through which drowsiness has been experimentally manipulated is also discussed. We conclude that by designing a hybrid drowsiness detection system that combines non-intusive physiological measures with other measures one would accurately determine the drowsiness level of a driver. A number of road accidents might then be avoided if an alert is sent to a driver that is deemed drowsy.

Recent research in the field of Human Computer Interaction aims at recognizing the user's emotional state in order to provide a smooth interface between humans and computers. This would make life easier and can be used in vast applications involving areas such as education, medicine etc. Human emotions can be recognized by several approaches such as gesture, facial images, physiological signals and neuro imaging methods. Most of the researchers have developed user dependent emotion recognition system and achieved maximum classification rate. Very few researchers have tried to develop a user independent system and obtained lower classification rate. Efficient emotion stimulus method, larger data samples and intelligent signal processing techniques are essential for improving the classification rate of the user independent system. In this paper, we present a review on emotion recognition using physiological signals. The various theories on emotion, emotion recognition methodology and the current advancements in emotion research are discussed in subsequent topics. This would provide an insight on the current state of research and its challenges on emotion recognition using physiological signals, so that research can be advanced to obtain better recognition.

Human emotion recognition plays a vital role in psychology, psycho-physiology and human machine interface (HMI) design. Electroencephalogram (EEG) reflects the internal emotional state changes of the subject compared to other conventional methods (face recognition, gestures, speech, etc). In this work, EEG signals are collected using 62 channels from 20 subjects in the age group of 21~39 years for determining discrete emotions. Audio-visual stimuli (video clips) is used for inducing five different emotions (happy, surprise, fear, disgust, neutral). EEG signals are preprocessed through Butterworth 4^th order filter with a cut off frequency of 0.5 Hz-60 Hz and smoothened using Surface Laplacian filter. EEG signals are framed into a short time duration of 5s and two statistical features (spectral centroid and spectral entropy) in four frequency bands namely alpha (8 Hz-16 Hz), beta (16 Hz-32 Hz), gamma (32 Hz-60 Hz) and alpha to gamma (8 Hz-60 Hz) are extracted using Fast Fourier Transform (FFT). These features are mapped into the corresponding emotions using two simple classifiers such as K Nearest Neighbor(KNN) and Probabilistic Neural Network (PNN). In this work, KNN outperforms PNN by offering the maximum mean classification accuracy of 91.33 % on beta band. This experimental results indicates the short time duration of EEG signals is highly essential for detecting the emotional state changes of the subjects.