A wearable device for monitoring and predicting alertness of an individual, the wearable device comprising: one or more sensors configured to obtain information signals about the individual, the sensors comprising at least one of: a motion sensor configured to produce movement data and/or body position data of the individual, a temperature sensor configured to produce distal skin temperature data of the individual, and a heart rate monitor configured to produce heart rate data of the individual; a memory configured to store: a default circadian rhythm configured to be refined with data derived from the information signals about the individual to generate an estimated circadian rhythm for the individual, and a bio-mathematical model configured to generate a fatigue score for the individual; a processor coupled to the one or more sensors and to the memory, configured to: receive the information signals about the individual including at least one of movement data, position data, distal skin temperature data, or heart rate data, demask the received information signals to remove obscuring signals caused by non-circadian events from the received information signals including distal skin temperature data and heart rate data of the individual, estimate a circadian rhythm of the individual by incorporating the demasked information signals about the individual to refine the default circadian rhythm, extract features from the demasked information signals about the individual and the estimated circadian rhythm, apply at least one pattern recognition algorithm or machine learning algorithm to the extracted features, extract at least one coefficient from the extracted features using the at least one pattern recognition algorithm or machine learning algorithm, apply a bio-mathematical model to the at least one extracted coefficient, and generate the fatigue score for the individual from the at least one extracted coefficient using the bio-mathematical model; and a support configured to support the one or more sensors, the memory, and the processor on the individual. one or more sensors configured to obtain information signals about the individual, the sensors comprising at least one of: a motion sensor configured to produce movement data and/or body position data of the individual, a temperature sensor configured to produce distal skin temperature data of the individual, and a heart rate monitor configured to produce heart rate data of the individual; a motion sensor configured to produce movement data and/or body position data of the individual, a temperature sensor configured to produce distal skin temperature data of the individual, and a heart rate monitor configured to produce heart rate data of the individual; a memory configured to store: a default circadian rhythm configured to be refined with data derived from the information signals about the individual to generate an estimated circadian rhythm for the individual, and a bio-mathematical model configured to generate a fatigue score for the individual; a default circadian rhythm configured to be refined with data derived from the information signals about the individual to generate an estimated circadian rhythm for the individual, and a bio-mathematical model configured to generate a fatigue score for the individual; a processor coupled to the one or more sensors and to the memory, configured to: receive the information signals about the individual including at least one of movement data, position data, distal skin temperature data, or heart rate data, demask the received information signals to remove obscuring signals caused by non-circadian events from the received information signals including distal skin temperature data and heart rate data of the individual, estimate a circadian rhythm of the individual by incorporating the demasked information signals about the individual to refine the default circadian rhythm, extract features from the demasked information signals about the individual and the estimated circadian rhythm, apply at least one pattern recognition algorithm or machine learning algorithm to the extracted features, extract at least one coefficient from the extracted features using the at least one pattern recognition algorithm or machine learning algorithm, apply a bio-mathematical model to the at least one extracted coefficient, and generate the fatigue score for the individual from the at least one extracted coefficient using the bio-mathematical model; and receive the information signals about the individual including at least one of movement data, position data, distal skin temperature data, or heart rate data, demask the received information signals to remove obscuring signals caused by non-circadian events from the received information signals including distal skin temperature data and heart rate data of the individual, estimate a circadian rhythm of the individual by incorporating the demasked information signals about the individual to refine the default circadian rhythm, extract features from the demasked information signals about the individual and the estimated circadian rhythm, apply at least one pattern recognition algorithm or machine learning algorithm to the extracted features, extract at least one coefficient from the extracted features using the at least one pattern recognition algorithm or machine learning algorithm, apply a bio-mathematical model to the at least one extracted coefficient, and generate the fatigue score for the individual from the at least one extracted coefficient using the bio-mathematical model; and a support configured to support the one or more sensors, the memory, and the processor on the individual.