  Heart rate and blink duration are two vital physiological signals which give
information about cardiac activity and consciousness. Monitoring these two
signals is crucial for various applications such as driver drowsiness
detection. As there are several problems posed by the conventional systems to
be used for continuous, long-term monitoring, a remote blink and ECG monitoring
system can be used as an alternative. For estimating the blink duration, two
strategies are used. In the first approach, pictures of open and closed eyes
are fed into an Artificial Neural Network (ANN) to decide whether the eyes are
open or close. In the second approach, they are classified and labeled using
Linear Discriminant Analysis (LDA). The labeled images are then be used to
determine the blink duration. For heart rate variability, two strategies are
used to evaluate the passing blood volume: Independent Component Analysis
(ICA); and a chrominance based method. Eye recognition yielded 78-92% accuracy
in classifying open/closed eyes with ANN and 71-91% accuracy with LDA. Heart
rate evaluations had a mean loss of around 16 Beats Per Minute (BPM) for the
ICA strategy and 13 BPM for the chrominance based technique.

Computer Vision and Pattern Recognition