Development of a Data-Driven Model to Predict Human Circadian Rhythms Based on Daily Light Exposure

This thesis develops a data-driven model to predict sleep-wake parameters based on daily light exposure. This is important because a new standard of light measurement has been introduced. The standard emphasizes the quantification of light according to the sensitivity of individual photoreceptors in the retina. This includes a newly discovered photoreceptor that is found to influence sleep-wake regulation significantly. The incorporation of this new standard ensures that the developed model accurately captures the non-visual effects of light on the human body. An improved model benefits real-world applications such as light intervention schemes for sleep-wake adjustments.