Application US20180289314 Consumer Sleep Technology

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United States of America

Application US20180289314

Published 2018-10-11

Method And System For Measuring, Predicting, And Optimizing Human Cognitive Performance

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.

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25 Independent Claims

1. A system comprising: a user interface having a display and a receiving means for receiving input from the user; at least one motion detection sensor capable of detecting movement by an individual wearing said at least one motion detection sensor, at least one memory configured to store an alertness model and data associated with the individual; and a processor in electrical communication with said user interface said at least one motion detection sensor, and said memory; said processor configured to receive a signal from said at least one motion detection sensor to monitor a level of activity of the user, store data obtained from the signal in said memory, when caffeine consumption information is received from said receiving means, store the caffeine consumption information in said memory as caffeine consumption data, determine a cognitive level for the user with the alertness model based on the stored data, the signal from said at least one motion detection sensor, and caffeine consumption data, and display the cognitive level on said display.
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30. A system comprising: a plurality of computing devices where each computing device is assigned to an individual, each of said computing devices having a user interface having a display and means for receiving input from the user; at least one motion detection sensor capable of detecting movement by an individual wearing said at least one motion detection sensor; at least one memory configured to store a sleep model and data associated with the individual; a communications module; and a processor in electrical communication with said user interface, said motion detection sensor, said communications module, and said memory; said processor configured to receive a signal from said at least one motion detection sensor to monitor a level of activity of the user, store data obtained from the signal in said memory, when caffeine consumption information is received from said receiving means, store the caffeine consumption information in said memory as caffeine consumption data, determine an cognitive level for the user with the alertness model based on the stored data, the signal from said at least one motion detection sensor, and caffeine consumption data, and display the cognitive level on said display; and a server capable of at least intermittent communication with each of said plurality of computing devices, said server configured to receive alertness model weights from said processors through respective communications modules, store received alertness model weights in a database associated with the individual associated with said computing device that sent the alertness model weights, and provide a planning interface to model different timing and amounts of sleep and caffeine consumption to provide a forecast for future cognitive levels or a regression for past cognitive levels for the user associated with the alertness model weights.
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38. A method for determining an individual's cognitive state using a computing device having a processor, a memory, a motion detection sensor, a display, and a receiving means for receiving input from a user about the individual, the method comprising: receiving a signal from the at least one motion detection sensor configured to monitor a level of activity of the user, storing data obtained from the signal in the memory, when caffeine consumption information is received from the receiving means, storing the caffeine consumption information in the memory as caffeine consumption data, determining a cognitive level for the user with the alertness model based on the stored data, the signal from the at least one motion detection sensor, and caffeine consumption data, and displaying the cognitive level on the display.
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