Abstract: Systems and methods are provided in which optimized driving trip schedules are generated, optimized, optionally scored according to multiple criteria including fatigue, and provided to a driver or other personnel. Trip schedules are generated from route plans connecting start and end waypoints and optionally intermediate waypoints. Hours-of-service (HoS) regulations and business objectives (fuel efficiency, time-constrained waypoints, etc.) are considered, where a forward greedy algorithm may be used to solve the problem of on-time delivery under such constraints. Driver sleep and fatigue are then determined from the generated trip schedules using sleep prediction models and fatigue prediction models. Trip schedules may then be scored, modified, and optimized in accordance with several other constraints.

Abstract: Systems and methods for analyzing the results of a stimulus-response test result of a subject with respect to those of a comparison population or subpopulation of interest are disclosed. A first set of testing conditions and/or demographic characteristics and their corresponding values are used optionally to identify a subpopulation of interest and select appropriate data from a general-population database. A second (and optionally a third) set of testing conditions and/or demographic characteristics (which may optionally be identical to the first) are then used to project either or both of the subject's test score or the test scores for the population or optional subpopulation of interest to a common basis of testing conditions and/or demographic characteristics using one or more projection functions specific to the testing condition and/or demographic characteristic, as applied to a particular test.

Abstract: Systems and methods for quantifiable assessment of vehicle driver performance based upon objective standards are disclosed. The physical and/or control states of a vehicle are monitored by sensors during a driving trip. Measurement data, optionally comprising a measurement signal, is composed from parameters selected from the measured physical and/or control states. The measurement data is then compared to reference data, optionally comprising a reference signal, comprising the same or similar physical and control state parameters, for the same or analogous driving trip or portion thereof, including discrete driving tasks, as determined by one or more of: a known driver of specific attributes, a population average, or an autonomous driving algorithm. A driver performance level may be determined as one or more characteristic metrics of a driving task, according to one or more path metrics of a driving task, or as a signal distance metric between the reference and measurement signals.

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 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: Systems and methods are disclosed for determining a fatigue level of a human operator of a motor vehicle based upon lane variability data and geographic position data of the vehicle, used either alone or in combination with other data such as (without limitation) vehicle operational data, vehicle environment data, road segments, and/or the like.

Abstract: Systems and methods to estimate a subject's sleep status over time by applying data-fusion algorithms to sleep data sets collected from multiple sleep data sources are disclosed. Embodiments employ Bayes' Theorem to combine sleep data from actigraphy, sleep diary, direct observation, sleep schedules, work schedules, performance tests, neurobehavioral tests and/or the like. Particular embodiments assign data error characteristics to each source, determine likelihoods of correct reporting of sleep status from each source, and apply Bayesian analysis to each source-specific likelihood to determine an overall sleep status estimate. Data error characteristics may account, without limitation, for data insertion errors, data deletion errors, and sleep timing errors. Heuristics may be also used to correct common errors found within collected sleep data and/or to infer sleep status from atypical sources of sleep data.

Abstract: Disclosed herein are methods for transforming numerical output of mathematical-fatigue models into contextual performance metrics, including without limitation, performance, incident and/or accident-related metrics associated with particular activities and/or with particular environments, such as but not limited to: the number and severity of injuries or cost of repairs associated with a particular incident, increases in insurance premiums, a performance rate, an error rate and/or the like.

Abstract: Method are provided for evaluating reaction time data obtained from a stimulus-response testing system. One exemplary method comprises: obtaining reaction time data, the reaction time data comprising a plurality of reaction times, each reaction time comprising an estimate of a time required for a subject to respond to a corresponding stimulus event; assigning a weight to each reaction time in the reaction time data in accordance with a weighting function, the weighting function comprising a rule that defines a mapping between reaction times and corresponding weights; and determining a weighted reaction time metric based at least in part on a sum of the weights assigned to the reaction times in the reaction time data.

Abstract: Methods are provided for calibrating stimulus-response test systems which include a stimulus output device for delivering a stimulus to a subject, and a response input device for receiving a response from the subject, said response input device comprising a response actuator that is capable of responding in human-like fashion to the stimulus-response test using variable motion control signals comprising one or more time offsets and one or more activation values, said activation values comprising variable speeds and forces for the response motion.

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 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: Methods are provided for calibrating stimulus-response test systems which include a stimulus output device for delivering a stimulus to a subject; and a response input device for receiving a response from the subject.

Abstract: Systems and methods for analyzing the results of a diagnostic-assessment test result of a subject with respect to those of a comparison population or subpopulation of interest are disclosed. A first set of testing conditions and/or demographic characteristics and their corresponding values are used optionally to identify a subpopulation of interest and select appropriate data from a general-population database. A second (and optionally a third) set of testing conditions and/or demographic characteristics (which may optionally be identical to the first) are then used to project either or both of the subject's test score or the test scores for the population or optional subpopulation of interest to a common basis of testing conditions and/or demographic characteristics using one or more projection functions specific to the testing condition and/or demographic characteristic, as applied to a particular test. A metric of comparison is then determined for the testing subject with this projected data.

Abstract: Systems and methods for quantifiable assessment of vehicle driver performance based upon objective standards are disclosed. The physical and/or control states of a vehicle are monitored by sensors during a driving trip. Measurement data, optionally comprising a measurement signal, is composed from parameters selected from the measured physical and/or control states. The measurement data is then compared to reference data, optionally comprising a reference signal, comprising the same or similar physical and control state parameters, for the same or analogous driving trip or portion thereof, including discrete driving tasks, as determined by one or more of: a known driver of specific attributes, a population average, or an autonomous driving algorithm. A metric of comparison may be determined as one or more characteristic metrics of a driving task, according to one or more path metrics of a driving task, or as a signal distance metric between the reference and measurement signals.

Abstract: Human neurobehavioral performance prediction systems and methods are disclosed in which disrupted sleep patterns, such as (without limitation) sleep fracturing due to apnea, are accounted for. Biomathematical models are used to predict neurobehavioral performance based on disrupted sleep using a sleep function modified in accordance with apnea-severity data to account for loss in sleep efficiency. Risk of diminished neurobehavioral performance can then be monitored in affected individuals. Compliance with treatment regimens, adjustments to apnea severity assessment, corrections to predicted future sleep schedules, and/or individualization of neurobehavioral performance model parameters can also be achieved based upon a comparison of actual and model-predicted performance levels.

Abstract: Systems and methods for inter-population assessment of neurobehavioral status employ neurobehavioral profiles to accommodate differing external conditions. Population profiles and external condition data are provided to a neurobehavioral performance model to determine neurobehavioral status under external conditions. Alternatively, neurobehavioral performance values may be retrieved from the profile when such values are stored in conjunction with external condition data. Comparisons of the resulting neurobehavioral status(es) are then determined, and may comprise without limitation one or more of: performance deltas, statistical parameter differences, rankings, above/below performance threshold determinations, pass/fail indicators, and countermeasure recommendations. Populations may comprise pluralities, individuals and empty (“null”) sets. Comparisons may also pertain to one or more relevant times of interest and one or more sets of testing conditions.

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: A normalized contextual performance metric quantifies the susceptibility of fatigue-related risk in a fatigue environment with activities conducted within a fatigue level range of interest. Fatigue incidents are quantified by one of a plurality of values associated with fatigue-incident measurement. Activities are quantified by one of a plurality of values associated with activity measurement. A normalized contextual performance metric is determined by identifying a fatigue level range of interest, summing all values of incidents occurring at the fatigue level range of interest, summing all values for relevant activities occurring at the fatigue level range of interest, and then dividing the first sum by the second. The normalized contextual performance metric thereby allows operational managers to assess risk of fatigue incidents by monitoring activities and fatigue levels within the fatigue environment.

Abstract: Disclosed herein are methods for transforming numerical output of mathematical-fatigue models into contextual performance metrics, including without limitation, performance, incident and/or accident-related metrics associated with particular activities and/or with particular environments, such as but not limited to: the number and severity of injuries or cost of repairs associated with a particular incident, increases in insurance premiums, a performance rate, an error rate and/or the like.