Method and system for measuring, predicting, and optimizing human cognitive performance
Inventors
Reifman, Jaques • Liu, Jianbo • Wesensten, Nancy • Balkin, Thomas • Ramakrishnan, Sridhar • Khitrov, Maxim Y.
Assignees
United States Department of the Army
Publication Number
US-11883194-B2
Publication Date
2024-01-30
Expiration Date
2036-06-08
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Abstract
A system, method and apparatus is disclosed, comprising a biomathetical model for optimizing cognitive performance in the face of sleep deprivation that integrates novel and nonobvious biomathematical models for quantifying performance impairment for both chronic sleep restriction and total sleep deprivation; the dose-dependent effects of caffeine on human vigilance; and the pheonotypical response of a particular user to caffeine dosing, chronic sleep restriction and total sleep deprivation in user-friendly software application which itself may be part of a networked system.
Core Innovation
This invention discloses a system, method, and apparatus comprising a biomathematical model designed to optimize human cognitive performance in the face of sleep deprivation. The model integrates components quantifying performance impairment due to both chronic sleep restriction and total sleep deprivation, the dose-dependent effects of caffeine on vigilance, and the phenotypical response of an individual user to caffeine dosing and sleep deprivation. This biomathematical model operates within a user-friendly software application that may be part of a networked system, accessible via portable computing devices like smartphones.
The problem addressed by this invention is the degradation of cognitive performance caused by sleep loss, which poses significant safety and productivity risks. Previous countermeasures, such as caffeine administration, are widely used, but existing tools and models fail to quantify or individually predict the performance-enhancing effects of caffeine under sleep deprivation. Furthermore, there is substantial inter-individual variability in response to sleep loss and caffeine, a trait-like variability that existing models do not adequately account for. Prior computational tools for fatigue risk management lack integration of caffeine effects and are not open-access or customizable to individual responses.
The system further allows exploration of future sleep schedules and caffeine dosing strategies to reach and sustain peak cognitive performance at desired times. The invention transforms physiological and behavioral data into actionable information enabling users to understand their current cognitive state and optimize future alertness by modulating sleep and caffeine intake.
Claims Coverage
The patent includes three independent claims encompassing method and system implementations for determining alertness levels using a biomathematical model that integrates sleep and caffeine data and individual responses.
Integrated alertness model with sleep and caffeine components
The alertness model incorporates a homeostatic component with a sleep debt lower asymptote that varies based on individual sleep debt and an upper asymptote, a circadian component with adjustable amplitude, and a caffeine component exerting multiplicative impact via exponential decay of caffeine dose over time using stored caffeine consumption data.
Model individualization via response time tests
The method includes performing response time tests (e.g., PVT) to measure alertness levels, determining offsets between model-predicted and tested alertness, and adjusting parameter weights within the alertness model to tailor it to an individual's sleep-loss phenotype.
Wearable system with real-time data processing and feedback
A system adapted to be worn by a user comprising a user interface, motion detection sensor, memory, and processor to receive activity signals and caffeine consumption data, convert signals to sleep-wake data, store data, determine and display alertness levels via the integrated model, perform recursive individualization using response time tests, and enable the user to act on alertness information to modulate sleep and caffeine consumption.
The claims cover a comprehensive system and method that combine physiological monitoring, individual cognitive performance testing, and biomathematical modeling of sleep, circadian rhythms, and caffeine pharmacodynamics to determine, predict, and optimize user-specific alertness levels with feedback-enabled interfaces and networked data management.
Stated Advantages
Allows real-time measurement and prediction of individual cognitive performance and alertness using an integrated model accounting for sleep deprivation and caffeine effects.
Enables individualization of the model to user-specific sleep-loss phenotypes through recursive adjustment based on response time testing.
Transforms physiological and behavioral data into actionable information supporting optimization of future sleep schedules and caffeine dosing to achieve desired cognitive performance levels.
Provides a user-friendly interface on portable devices and supports networked systems for group-level planning and management of cognitive performance.
Documented Applications
Optimizing duty-time alertness and minimizing fatigue-related errors and accidents in occupational settings, particularly in 24-hour operations and shift-work schedules.
Individual cognitive performance assessment and prediction in civilian and military contexts affected by sleep deprivation.
Workforce scheduling and fatigue risk management through networked systems analyzing and projecting cognitive states across multiple users.
Health and performance monitoring using wearable devices integrated with smartphones or tablets to track sleep, caffeine intake, and alertness levels.
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