Abstract: Unlike state of art eye blink detection techniques that are generalized for usage across individuals affecting accuracy of eye blink prediction from subject to subject, embodiments of the present disclosure provide a method and system for personalized eye blink detection using passive camera-based approach. The method first generates a subject specific annotation data, which is then further processed to derive subject specific personalized blink threshold values. The method disclosed provides three unique approaches to compute the personalized blink threshold values which is one time calibration process. The personalized blink threshold values are then used to generate a binary decision vector (D) while analyzing input test images (video sequences) of the subject of interest. Further, values taken by elements of the decision vector (D) are analyzed for a predefined time period to predict possible eye blinks of the subject.

Abstract: This disclosure relates generally to a method and a system for determining quality of PPG signal. The PPG signals are extensively used for deducing health parameters of patients to infer the physiological conditions of heart, blood pressure, breathing patterns of the patients. However, analysis based on PPG signals is extremely challenging and is accurate only on high quality PPG signals. However, the existing techniques for determining quality of PPG signal (that are collected using wearable devices) require huge training or use complicated algorithms and cannot be used for real-time analysis. The disclosed methods and system for PPG quality assessment is based on the frequency domain analysis, wherein heart and respiratory components in the frequency spectrum are used effectively to derive the quality checker metric which is further used to estimate a plurality of optimal thresholds that is used for determining the quality of PPG signals at real-time.