Abstract: Systems and methods are provided for generating individualized predictions of alertness or performance for human subjects. Alertness or performance predictions may be individualized to incorporate a subject's individual traits and/or individual states. These individual traits and/or individual states (or parameters which represent these individual traits and/or individual states) may be represented by random variables in a mathematical model of human alertness. The mathematical model and/or prediction techniques may incorporate effects of the subject's sleep timing, the subject's intake of biologically active agents (e.g. caffeine) and/or the subject's circadian rhythms. The mathematical model and/or prediction techniques may incorporate feedback from the subject's measured alertness and/or performance.

Abstract: Systems and methods are provided for predicting a circadian state of an individual. The methods comprise: providing a model representative of the response of the circadian state to light stimulus, the model comprising at least one model variable representative of a probability distribution function (PDF) of a phase offset of the circadian state of the individual; and using the model to estimate an updated PDF of the phase offset, wherein using the model to estimate the updated PDF of the phase offset comprises performing a Bayesian estimation process commencing with an initial PDF of the phase offset and iterating toward the updated PDF of the phase offset.

Abstract: Systems and methods are provided for generating individualized) predictions of alertness or performance for human subjects. Alertness or performance predictions may be individualized to incorporate a subject's individual traits and/or individual states. These individual traits and/or individual states (or parameters which represent these individual traits and/or individual states) may be represented by random variables in a mathematical model of human alertness. The mathematical model and/or prediction techniques may incorporate effects of the subject's sleep timing, the subject's intake of biologically active agents (e.g. caffeine) and/or the subject's circadian rhythms. The mathematical model and/or prediction techniques may incorporate feedback from the subject's measured alertness and/or performance.

Abstract: Systems and methods are provided for predicting a circadian state of an individual. The methods comprise: providing a model representative of the response of the circadian state to light stimulus, the model comprising at least one model variable representative of a probability distribution function (PDF) of a phase offset of the circadian state of the individual; and using the model to estimate an updated PDF of the phase offset, wherein using the model to estimate the updated PDF of the phase offset comprises performing a Bayesian estimation process commencing with an initial PDF of the phase offset and iterating toward the updated PDF of the phase offset.

Abstract: Aspects of the invention provide systems and methods for controllably adjusting the circadian pacemaker cycle of a subject using light (or other stimulus) through application of model-based predictive control techniques. This approach allows the use of closed-loop feedback to compensate for modeling errors, unknown initial conditions and disturbances. It also allows an optimal level of light (or other stimulus) to be generated based on minimization of a cost function. The cost function may incorporate a term associated with tracking errors and a term associated with the amount of light used. The tracking function may be minimized subject to one or more constraints which may include a minimum and maximum amount of light (or other stimulus).