Abstract: The present invention relates, in various embodiments, to prediction of human subject states (e.g. physiological and/or psychological and/or neurological state) via a hybrid approach, which includes elements of AI-based classification and objective physiological analysis (such as blepharometric analysis). Embodiments are described by reference to applications in driver alertness monitoring. However, it will be appreciated that the technology is not limited as such, and has application in a broader range of context. For example, the technology is applicable to prediction of physiological states other than alertness level, and to implementation environments other than driver monitoring.

Abstract: Lower sampling rates have been found to provide sufficient data for use in the method of U.S. Pat. Nos. 7,071,831, 7,616,125 and 7,791,491. The method of determining drowsiness includes the steps of receiving eye movement data collected at sampling rates as low as 20 Hz. The data is preferentially interpolated to provide a data set at a higher sampling rate in the order of 500 Hz. For each data point, values of amplitude and velocity of eye movement and whether the measures relate to eyelid opening or closing are derived. An algorithm is then used to obtain values of the amplitude to velocity ratios of eyelid opening and closing and using these values in an algorithm for providing a measure of drowsiness. With this method eyelid and eye movement may be monitored using any suitable technology or sensor including video or digital camera technology to identify and measure the appropriate ocular movements.