Abstract: Systems and methods are provided to collect biometrically-verified actigraphy data, comprising: obtaining and processing a subject's measured biometric input from a biometric sensor to generate a current biometric signature of the subject; verifying the subject's identity by comparing the current biometric signature to one previously obtained from a database and evaluating a proximity metric of the current biometric signature of the subject to the previously obtained biometric signature from the database; if the subject passes identity verification, obtaining actigraphy data from an actigraphy sensor worn by the subject; at one or more times while obtaining the actigraphy data, repeating the steps of obtaining and processing a subject's measured biometric input from a biometric sensor to generate a current biometric signature of the subject, and verifying the identity of the subject to ensure that the identity of the subject passes the identity verification at the one or more times.

Abstract: Distributed computing methods and systems are disclosed, wherein intensive fatigue-risk calculations are partitioned according to available computing resources, parameters of the fatigue-risk calculation, time-sensitive user demands, and the like. Methods are disclosed wherein execution-cost functions are used to allocate accessible computing resources. Additional methods include partitioning calculation tasks by user-prioritized needs and by general mathematical features of the calculations themselves. Included herein are methods to calculate only prediction-maximum likelihoods instead of full probability distributions, to calculate prediction likelihoods using Bayesian prediction techniques (instead of full re-tabulation of all data), to collate interim results of fatigue-risk calculations where serial results can be appropriately collated (e.g., serial time-slice independence of the cumulative task involved), to use simplified (e.g.

Abstract: Distributed computing methods and systems are disclosed, wherein intensive fatigue-risk calculations are partitioned according to available computing resources, parameters of the fatigue-risk calculation, time-sensitive user demands, and the like. Methods are disclosed wherein execution-cost functions are used to allocate accessible computing resources. Additional methods include partitioning calculation tasks by user-prioritized needs and by general mathematical features of the calculations themselves. Included herein are methods to calculate only prediction-maximum likelihoods instead of full probability distributions, to calculate prediction likelihoods using Bayesian prediction techniques (instead of full re-tabulation of all data), to collate interim results of fatigue-risk calculations where serial results can be appropriately collated (e.g., serial time-slice independence of the cumulative task involved), to use simplified (e.g.

Abstract: Systems and methods are provided to collect biometrically-verified actigraphy data, comprising: obtaining and processing a subject's measured biometric input from a biometric sensor to generate a current biometric signature of the subject; verifying the subject's identity by comparing the current biometric signature to one previously obtained from a database and evaluating a proximity metric of the current biometric signature of the subject to the previously obtained biometric signature from the database; if the subject passes identity verification, obtaining actigraphy data from an actigraphy sensor worn by the subject; at one or more times while obtaining the actigraphy data, repeating the steps of obtaining and processing a subject's measured biometric input from a biometric sensor to generate a current biometric signature of the subject, and verifying the identity of the subject to ensure that the identity of the subject passes the identity verification at the one or more times.