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WO2025101707A1 - Modélisation informatique de prédictions de repos discrétionnaires - Google Patents

Modélisation informatique de prédictions de repos discrétionnaires Download PDF

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Publication number
WO2025101707A1
WO2025101707A1 PCT/US2024/054864 US2024054864W WO2025101707A1 WO 2025101707 A1 WO2025101707 A1 WO 2025101707A1 US 2024054864 W US2024054864 W US 2024054864W WO 2025101707 A1 WO2025101707 A1 WO 2025101707A1
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WO
WIPO (PCT)
Prior art keywords
slot
slots
discretionary
rest
subset
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PCT/US2024/054864
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English (en)
Inventor
Hans P.A. VAN DONGEN
Mark E. MCCAULEY
Peter McCauley
Leonid V. KALACHEV
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University of Montana Missoula
Washington State University WSU
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University of Montana Missoula
Washington State University WSU
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Publication of WO2025101707A1 publication Critical patent/WO2025101707A1/fr
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    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G06Q10/063—Operations research, analysis or management
    • G06Q10/0631—Resource planning, allocation, distributing or scheduling for enterprises or organisations
    • G06Q10/06311—Scheduling, planning or task assignment for a person or group
    • G06Q10/063114—Status monitoring or status determination for a person or group
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G06Q10/063—Operations research, analysis or management
    • G06Q10/0631—Resource planning, allocation, distributing or scheduling for enterprises or organisations
    • G06Q10/06311—Scheduling, planning or task assignment for a person or group
    • G06Q10/063116—Schedule adjustment for a person or group
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G06Q10/063—Operations research, analysis or management
    • G06Q10/0639—Performance analysis of employees; Performance analysis of enterprise or organisation operations
    • G06Q10/06398—Performance of employee with respect to a job function
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/10—Office automation; Time management
    • G06Q10/105—Human resources
    • G06Q10/1057—Benefits or employee welfare, e.g. insurance, holiday or retirement packages
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
    • G06Q50/10—Services
    • G06Q50/22—Social work or social welfare, e.g. community support activities or counselling services

Definitions

  • Computer modeling is an important tool that uses heuristics and computational power to simulate, analyze, and predict phenomena or behaviors.
  • computer modeling involves using computer software to create virtual representations, such as digital models in computer codes that describe target systems or processes.
  • Such digital models can range from mathematical equations to intricate simulations involving multiple variables and interactions in multiple dimensions.
  • users can simulate or predict how the target systems or processes would behave in response to different conditions or events.
  • computer modeling can allow users to explore scenarios, evaluate hypotheses, predict outcomes, and make informed decisions without costly and timeconsuming physical experiments.
  • computer modeling can be an important tool for simulating and analyzing target systems or processes.
  • computer modeling relies heavily on historical data to function effectively.
  • Historical data serves as a foundation that provides empirical evidence for accurate and dependable predictions from the computer models.
  • computer models for weather forecasting often draw on decades or even centuries of temperature, precipitation, and atmospheric data to simulate long-term changes in climate patterns.
  • Economists utilize historical market and financial data to model economic trends, analyze economic cycles, and forecast future economic trends.
  • epidemiology historical data on disease outbreaks have been used to model spreads of infectious diseases and to evaluate the potential impact of public health interventions.
  • a data-driven approach for computer modeling allows for testing of hypotheses, validation of theories, and exploration of scenarios based on historical records.
  • historical data may be unavailable, too costly to obtain, or too dynamic to be useful for predicting future behaviors, such as in the field of fatigue risk management.
  • Fatigue from sleep loss or circadian rhythm interruptions can degrade cognitive functioning and necessitates use of countermeasures to maintain performance, productivity, and safety. Errors and accidents resulting from fatigue can cause considerable personal, economic, and societal costs. Fatigue risk management is thus designed for predicting, preventing, and/or mitigating fatigue and related negative consequences.
  • a useful tool for fatigue risk management is biomathematical fatigue modeling, i.e., prediction of periods of elevated fatigue levels based on scientific knowledge of biology related to human sleep regulation and circadian rhythm icity.
  • Accurate biomathematical fatigue modeling can be based on certain information, such as when people can sleep and when people would indeed sleep when given an opportunity. In operational settings involving people working long hours and through the night, knowing when people can sleep is usually straightforward (e.g., when off duty, not driving to/from work, and not otherwise required to be awake). Yet, knowing when people would indeed sleep when given an opportunity turns out to be quite challenging especially when people travel across time zones.
  • Several embodiments of the disclosed technology are directed to computer modeling for predicting when discretionary rest would occur without collecting historical records of sleep data.
  • the disclosed technology is based on 1 ) scientific knowledge regarding biology of sleep/wake regulation, including when wakefulness is naturally induced (low probability for sleep), when sleep is naturally induced (high probability for sleep), and when sleep is biologically discretionary; 2) scientific knowledge regarding the level of fatigue that arises when sleep is curtailed or displaced as well as risks to performance, productivity, and safety associated with such fatigue; 3) a realization that biological regulation of sleep is reactive, i.e.
  • Such an understanding is based in the biology of sleep/wake regulation, such that proactive rest is best placed when a probability for sleep is highest during an interval (of, e.g., about one or two days) preceding a duty period because such a rest yields the greatest probability that sleep would occur and be expected to most effectively reduce the anticipated high levels of future fatigue to be below the preset threshold. It is also believed that workers would seek to be efficient in the use of time for such discretionary rest. To that end, a worker would plan a discretionary rest to be just long enough to adequately mitigate the anticipated high levels of future fatigue but not any longer. As such, with the discretionary rest, the future fatigue can be reduced to be just below the preset threshold, e.g., less than 10%, 5%, 1 %, or any other suitable percentages from the preset threshold.
  • a lookback window can be about one to two days, such as thirty-six hours or any other suitable lengths of time.
  • a lookback window can contain one or more discretionary rest opportunities (i.e. , one or more periods when sleep is not limited by duty periods or constrained by sleep biology).
  • One objective of the disclosed technology is to predict placement of discretionary rest period(s) such that any resulting rest reduces future fatigue during a subsequent duty period to be just below an acceptable level while a probability of the discretionary rest occurring is highest or near-highest and the rest duration is minimal or near-minimal.
  • aspects of the disclosed technology are directed to dividing a duty schedule into “time bins” or “slots” each with a length of 5, 15, 30, or other suitable numbers of minutes, iteratively estimating an optimal or near-optimal timing of discretionary rest at a time bin, assessing the effect of the discretionary rest on the anticipated fatigue during a subsequent duty period, and repeating the foregoing operations for other time bins until the anticipated fatigue during the duty period has been adequately mitigated (e.g., at least not exceeding the preset threshold).
  • Figure 1 is a schematic diagram illustrating a computer-implemented model generator configured to predict proactive discretionary rest without historical records in accordance with embodiments of the disclosed technology.
  • Figure 2 is a flow chart illustrating a process of preprocessing data of duty schedule for predicting discretionary rest in accordance with embodiments of the disclosed technology.
  • Figure 3 is a flow chart illustrating a process of determining the state of a homeostatic process for predicting discretionary rest in accordance with embodiments of the disclosed technology.
  • Figure 4 is a flow chart illustrating a process of iteratively examining time bins of a duty schedule for possible discretionary rest in accordance with embodiments of the disclosed technology.
  • Figure 5 is a flow chart illustrating a process of searching for an optimal or near-optimal placement of a discretionary rest period that can efficiently mitigate fatigue during a subsequent duty period if discretionary rest is taken in accordance with embodiments of the disclosed technology.
  • Figures 6A-6C are charts of homeostatic pressure versus time-of-day illustrating example homeostatic and circadian processes under various scenarios in accordance with embodiments of the disclosed technology.
  • Figure 7 is a computing device suitable for executing at least some of the components of the computer-implemented model generator in Figure 1.
  • a data-driven approach for computer modeling can allow for the testing of hypotheses, validation of theories, and exploration of scenarios.
  • historical data may be unavailable, too costly to obtain, or too dynamic to be useful for predicting future behaviors, such as in the field of fatigue risk management.
  • sleep/wake regulation strongly impacts timing and duration of sleep.
  • the biological processes i.e., a circadian process that tracks time of day and a homeostatic process that tracks a balance between time spent awake and time spent asleep, drive sleep to occur at night in a single consolidated period approximately eight hours in duration.
  • One approach to mitigate the foregoing fatigue-related risks is through proactive discretionary rest, e.g., daytime napping before a duty period. Based on guidance from employers, past personal experience, intuition, or other sources, use of this approach is common in around-the-clock operations such as emergency medical services, passenger and cargo flight operations, the military, etc.
  • the effectiveness of proactive discretionary rest depends on the homeostatic and circadian processes of sleep/wake regulation. The homeostatic process builds up a pressure for sleep in a saturating exponential manner when a person is awake. The pressure then dissipates in a saturating exponential manner when a person is asleep.
  • the circadian process modulates two thresholds in a near-sinusoidal fashion across the twenty-four hours of the day.
  • the upper threshold or “sleep threshold” delineates a sleep-inducing zone.
  • the lower threshold or “wake threshold” delineates a wake-inducing zone.
  • sleep is biologically neither induced nor avoided.
  • This discretionary zone for sleep is where people may begin a proactive rest period, or end a sleep period by, for example, setting an alarm.
  • Figures 6A-6C show homeostatic pressure versus time-of-day charts illustrating example homeostatic and circadian processes in three different scenarios.
  • the solid line represents a homeostatic process, i.e., higher in the graph denoting greater sleep pressure.
  • the dashed line represents an upper threshold modulated by the circadian process to delineate the sleep-inducing zone.
  • the dotted line represents a lower threshold modulated by the circadian process to delineate the wake-inducing zone.
  • Figure 6A shows a scenario in which sleep is free of any relevant constraints and naturally occurs at night in a consolidated period of approximately eight hours.
  • sleep begins naturally when the homeostatic process that builds up homeostatic pressure during wakefulness exceeds the sleep threshold and reaches the sleep-inducing zone.
  • sleep terminates naturally when the homeostatic process that dissipates homeostatic pressure during sleep exceeds the wake threshold and reaches the wake-inducing zone. The net result is that sleep occurs at night in a consolidated period of approximately eight hours.
  • Figure 6B shows an example of nighttime sleep being displaced by a nighttime duty period (e.g., a night shift).
  • a nighttime duty period e.g., a night shift.
  • the homeostatic pressure exceeds the sleep threshold and reaches the sleep inducing zone, no sleep occurs because the worker is still awake.
  • the excess homeostatic pressure within the sleepinducing zone causes fatigue while the worker is awake at night.
  • the excess homeostatic pressure reactively triggers daytime sleep as soon as the worker’s duty period ends to reduce the homeostatic pressure to be below the sleep threshold.
  • the reactive nature of natural sleep regulation indicates that recovery does not occur until after fatigue has already occurred.
  • sleep is displaced and begins as soon as an opportunity for sleep arises after a duty period. Sleep ends naturally when the homeostatic pressure falls below the wake threshold and reaches the wake-inducing zone even though the duration of sleep is curtailed, and recuperation is incomplete.
  • Figure 6C shows a proactive placement of a discretionary rest (e.g., a daytime nap) before a duty period to mitigate anticipated high levels of fatigue.
  • the discretionary rest in this scenario is a choice behavior constrained but not dictated by biology.
  • predicting proactive discretionary rest is not just predicting the dynamics of sleep/wake biology but also behaviors based on discretionary decision making.
  • the inventors have recognized that, based on a biology-influenced predictability, discretionary rest is highly probable for mitigating anticipated high levels of future fatigue.
  • a discretionary rest period e.g., proactive daytime nap between about 19:00 and 21 :00 in the evening
  • the proactive daytime nap sufficiently reduced the homeostatic pressure of the worker at the beginning of the duty period such that the homeostatic pressure never exceeds the sleep threshold throughout the entire duty period.
  • the worker decides not to take another discretionary rest after the night shift, and the next day sleep naturally begins earlier in the evening to compensate for the lost sleep.
  • Several embodiments of the disclosed technology can provide prediction of when people would proactively take discretionary rest in around-the-clock operations and other settings without requiring collecting historical records.
  • several embodiments of the disclosed technology are configured to iteratively estimate an optimal timing of a discretionary rest period (e.g., 5, 15, or 30 minutes), assessing any effect on the anticipated fatigue during a subsequent duty period given discretionary rest, and repeat the foregoing operations for another period until the anticipated fatigue during the duty period has been adequately mitigated.
  • a discretionary rest period e.g., 5, 15, or 30 minutes
  • At least certain aspects of the disclosed technology can be readily implemented to evaluate an entire duty schedule with multiple duty periods that each may or may not involve proactive rest behavior and can be easily integrated with existing biomathematical fatigue models.
  • several embodiments of the disclosed technology can also utilize the available historical records to augment the expected prediction accuracy.
  • Figure 1 is a schematic diagram illustrating a computer-implemented model generator 100 configured to predict proactive discretionary rest without historical records in accordance with embodiments of the disclosed technology.
  • individual software components, objects, classes, modules, and routines may be a computer program, procedure, or process written as source code in C, C++, C#, Java, and/or other suitable programming languages.
  • a component may include, without limitation, one or more modules, objects, classes, routines, properties, processes, threads, executables, libraries, or other components. Components may be in source or binary form.
  • Components may also include aspects of source code before compilation (e.g., classes, properties, procedures, routines), compiled binary units (e.g., libraries, executables), or artifacts instantiated and used at runtime (e.g., objects, processes, threads).
  • aspects of source code before compilation e.g., classes, properties, procedures, routines
  • compiled binary units e.g., libraries, executables
  • artifacts instantiated and used at runtime e.g., objects, processes, threads.
  • a system can comprise a first component, a second component, and a third component operatively coupled to one another.
  • the foregoing components can, without limitation, encompass a system that has the first component being a property in source code, the second component being a binary compiled library, and the third component being a thread created at runtime.
  • the computer program, procedure, or process may be compiled into object, intermediate, or machine code and presented for execution by one or more processors of a personal computer, a tablet computer, a network server, a laptop computer, a smartphone, and/or other suitable computing devices.
  • components may include hardware circuitry.
  • hardware may be considered fossilized software, and software may be considered liquefied hardware.
  • software instructions in a component may be burned to a Programmable Logic Array circuit or may be designed as a hardware component with appropriate integrated circuits.
  • hardware may be emulated by - software.
  • Various implementations of source, intermediate, and/or object code and associated data may be stored in a computer memory that includes read-only memory, random-access memory, magnetic disk storage media, optical storage media, flash memory devices, and/or other suitable computer readable storage media.
  • computer readable storage media excludes propagated signals.
  • the data preprocessor 102 can be configured to receive data representing a duty schedule 101 of one or more workers.
  • the data of duty schedule 101 can include days and time durations during which the one or more workers are on/off duty based on planned or actual work schedules, or work schedules augmented with information about other demands (e.g., preparation time, commutes, training and other non-operational duties, rest requirements/regulations, etc.) and/or on-the-job rest opportunities.
  • duty periods are used herein to demonstrate various aspects of the disclosed technology, embodiments of the disclosed techniques can also be applied for other activity periods that may be performance-critical, safety-critical, or otherwise of interest.
  • the data preprocessor 102 can also be configured to validate the received duty schedule 101 (e.g., by checking for time conflicts) and indicating data errors based on certain preset criteria.
  • the data preprocessor 102 can be configured to format, segment, initialize, or otherwise prepare the received data of duty schedule 101 for further processing by the prediction engine 104.
  • the data preprocessor can represent the duty schedule 101 in a computer memory by an array of time bins that are marked either “On duty” or “not on duty.”
  • the data preprocessor 102 can also generate another array of time bins to keep track of times when sleep is scheduled. Yet another array of time bins can be created to track whether a time bin has been examined for proactive discretionary rest.
  • the data preprocessor 102 can assign an initial value of the homeostatic pressure by computing a state of the homeostatic process from the worker’s sleep/wake history or by making a reasonable assumption (e.g., that the worker is well-rested at the start of the duty schedule).
  • One example data preparation process 110 performable by the data preprocessor 102 is discussed below with reference to Figure 2.
  • the homeostatic pressure module 104 can be configured to determine a state of the homeostatic process for a worker for a particular time bin.
  • the state of the homeostatic process can be represented by a homeostatic pressure or a fatigue level based on a circadian process of the worker.
  • the state of the homeostatic can be represented by a combination of the foregoing parameters and/or other suitable parameters.
  • alternative sets of equations and parameter values can also be suitable for determining the state of the homeostatic process.
  • An example process 150 for determining the state of homeostatic process performable by the data preprocessor 102 is discussed below with reference to Figure 3.
  • the prediction engine 106 can be configured to iteratively examine time bins of the received duty schedule 101 for possible discretionary rest.
  • the prediction engine 106 can start a loop for examining a time bin or slot at an initial time ti, which can be a user selected beginning time bin in the duty schedule 101 , a beginning duty period in the duty schedule 101 , or other suitable times.
  • the prediction engine 106 can then be configured to determine whether (1 ) a subsequent time bin ti+i at falls at least in part in a duty period according to the duty schedule 101 ; and (2) a fatigue level in the time bin ti+i exceeds a preset fatigue threshold.
  • the prediction engine 106 can be configured to invoke the lookback module 108 that can iteratively search for an optimal or near-optimal placement of a period prior to the duty period at ti+i such that discretionary rest during the period mitigates fatigue during the duty period.
  • the prediction engine 106 can be configured to assign a state of “Sleep,” “Wake,” or maintain a prior state based on a homeostatic pressure level for the time bin at ti+i . After examining the time bin at ti, the prediction engine 106 can be configured to examine another time bin in a similar fashion by, for example, incrementing a slot count by a preset quantity, e.g., by one.
  • the prediction engine 106 can generate and output a prediction result 103 that shows not only reactive but also proactive discretionary rest period(s) of the worker based on both the homeostatic and circadian processes.
  • An example process 120 for iteratively examining time bins for possible discretionary rest is discussed in more detail below with reference to Figure 4.
  • the lookback module 108 can be configured to search for an optimal or near- optimal placement of a discretionary rest period that can efficiently mitigate fatigue during a subsequent duty period if discretionary rest is taken.
  • the lookback module 108 can be configured to isolate a lookback window as a set of time bins immediately prior to ti+i. Within the defined lookback window, the lookback module 108 can be configured to generate a lookback subset of time bins that are (1 ) not in a “Sleep” state; (2) not in a “On duty” state; and (3) not having a flag indicating the time bin(s) have been examined. The lookback module 108 can then determine whether the lookback subset includes any time bins at all.
  • the lookback module 108 can be configured to terminate execution and indicate to the prediction engine 106 that no solution can be found. If at least one time bin, e.g., at time tj is present in the lookback subset, the lookback module 108 can be configured to locate a time bin at tk at which the worker has the highest fatigue level and assign a “Sleep” state to the time bin at tk.
  • the fatigue level can be defined as a homeostatic pressure relative to the lower threshold. In other implementations, the fatigue level may be defined in other suitable manners.
  • the lookback module 108 can then be configured to determine whether setting a “Sleep” state to the time bin at tk would cause any time bins with a “Sleep” state in the lookback window to have a homeostatic pressure less than the wake threshold. If at least one time bin with a “Sleep” state in the lookback window has a homeostatic pressure less than the wake threshold, the lookback module 108 can be configured to reassign a “Wake” state to the time bin at tk and mark the time bin at tk as examined. Otherwise, the lookback module 108 maintains the assigned “Sleep” state at the time bin tk and mark the time bin as examined.
  • the lookback module 108 can then be configured to determine whether the fatigue level at the time bin at ti+i is still above the fatigue threshold. If not, the lookback module 108 returns results to the prediction module 106. Otherwise, the lookback module 108 reverts to defining another lookback subset in an iterative manner until the fatigue level of the time bin at ti+i is no longer above the fatigue threshold, or all time bins in the lookback window has been examined, whichever comes first.
  • An example process 160 of searching for an optimal or near-optimal placement of a discretionary rest period that can efficiently mitigate fatigue during a subsequent duty period if discretionary rest is taken is discussed below with reference to Figure 5.
  • Figure 2 is a flow chart illustrating a process 110 of preprocessing data of duty schedule 101 ( Figure 1) for predicting discretionary rest in accordance with embodiments of the disclosed technology.
  • the data preprocessor 102 ( Figure 2) can be configured to execute some or all of the stages of process 110.
  • other suitable component(s) of the model generator 100 can be configured to execute at least one of the stages of process 110 described below.
  • the process 110 can include an optional stage 111 for validating the received data of duty schedule 101 ( Figure 1 ).
  • the duty schedule 101 can be analyzed for time conflicts; for example, a time period may be marked as both on duty and off duty.
  • validating the received data can include checking for authenticity (e.g., based on digital certificates/signatures) or indicating data errors based on certain preset criteria.
  • the validating stage 110 may be omitted from the process 110.
  • the process 110 can also include creating one or more arrays of time bins or slots based on the received duty schedule at stage 112.
  • a sleep array 113 In the embodiment shown in Figure 2, a duty array 115, and an exam array 117 are shown for illustration purposes.
  • the sleep array 113 can include columns and rows of data fields representing, for instance, a start time of a time bin, an end time of a time bin, and a data field containing data indicating a state of “Sleep” or “Wake.”
  • the duty array 115 can include columns and rows of data fields representing, for instance, a start time of a time bin, an end time of a time bin, and a data field containing data indicating a state of “On duty” or “Off duty.”
  • the exam array 117 can include columns and rows of data fields representing, for instance, a start time of a time bin, an end time of a time bin, and a data field containing data indicating whether a time bin has been “Examined” or “Not examined.”
  • the foregoing arrays may include additional and/or different data fields, such as those containing data indicating a sequence number, count, or other suitable information.
  • arrays, tables, or other suitable data structures in addition to or in lieu of
  • the process 110 can then include one or more stages to initialize certain data fields in the foregoing arrays.
  • the process 110 can include setting all slots in the sleep array 113 to “Wake” at stage 114 and setting all slots in the exam array 117 to “Not examined” at stage 116 in parallel.
  • initialization of the data fields in the foregoing arrays can be performed in sequence, staggered, or in other suitable manners.
  • Figure 3 is a flow chart illustrating a process 150 of determining the state of a homeostatic process for predicting discretionary rest in accordance with embodiments of the disclosed technology.
  • the homeostatic pressure module 104 ( Figure 1 ) can be configured to execute some or all of the stages of process 150.
  • other suitable component(s) of the model generator 100 ( Figure 1 ) can be configured to execute at least one of the stages of process 150 described below.
  • the process 150 can include a decision stage 152 to determine whether time bin ti is associated with a sleep state.
  • the foregoing determination can be based on data contained in a data field in the sleep array 113 ( Figure 2) corresponding to the time bin ti.
  • the determination can be based on a combination of the data in one or more data fields in the sleep or other arrays.
  • the process 150 can include decreasing a homeostatic pressure level by one increment at stage 154.
  • the process 150 can include increasing a homeostatic pressure level by one increment at stage 156.
  • the increment of homeostatic pressure can be preset, for example, based on a time of day, a duration of the time bin, or other factors.
  • the process 150 includes assigning the derived homeostatic pressure level to a time bin at ti, for example, by modifying data stored in the sleep array 113 ( Figure 2) or other suitable data structures. The operation of the process 150 then returns.
  • Figure 4 is a flow chart illustrating a process 120 of iteratively examining time bins of a duty schedule for possible discretionary rest in accordance with embodiments of the disclosed technology.
  • the prediction engine 106 ( Figure 1 ) can be configured to execute some or all of the stages of process 120.
  • other suitable component(s) of the model generator 100 ( Figure 1 ) can be configured to execute at least one of the stages of process 120 described below.
  • the initial time bin can be a slot at a starting point of the duty schedule 101.
  • the initial time bin can be a slot at a first duty period in the duty schedule 101 or can be one of other suitable slots in the duty schedule 101.
  • Example operations of performing the lookback procedure are described in more detail below with reference to Figure 5.
  • the determination at stage 126 can also be based on risks (e.g., probability, magnitude, and/or duration of exposure to one or more adverse consequences associated with fatigue or risks that account for other non-fatigue-related risk factors.
  • the process 150 proceeds to another decision stage 142 to determine whether additional slots in the duty schedule 101 are yet to be examined. In response to determining that additional slot(s) exist in the duty schedule 101 , the process 150 proceeds to incrementing the slot count by a step, e.g., one before reverting to determining a homeostatic pressure at stage 122. In response to determining that no additional slots exist in the duty schedule 101 are yet to be examined, the process 150 proceeds to outputting a sleep/wake prediction result at stage 146.
  • Figure 5 is a flow chart illustrating a process 160 of searching for an optimal or near-optimal placement of a discretionary rest period in a lookback window that can mitigate high fatigue during a subsequent duty period if discretionary rest is taken in accordance with embodiments of the disclosed technology.
  • the lookback window includes a time interval immediately preceding a time bin at which high fatigue is detected.
  • the lookback window can be twenty four hours, thirty six hours, forty eight hours, or any other suitable durations.
  • the lookback module 108 ( Figure 1 ) can be configured to execute some or all of the stages of process 160.
  • other suitable component(s) of the model generator 100 Figure 1
  • the process 160 can include identifying a lookback subset of time bins that do not have an “Sleep” state, do not have a “On duty” state, and for which no previous attempt to place a proactive discretionary rest period been made, i.e. , have a “Not examined” state in corresponding data fields of the sleep array 113, the duty array 115, and the exam array 117, respectively, at stage 162.
  • the process 160 can then include a decision stage 163 to determine whether the lookback subset contains any time bins or slots. In response to determining that the lookback subset does not contain any time bins or slots, the process 160 returns. The absence of any time bins in the lookback subset would indicate that previous assessments in the lookback procedure have examined all proactive discretionary rest possibilities, and thus no further proactive fatigue mitigation is possible.
  • a difference between the homeostatic pressure and the sleep threshold ( Figures 6A-6C) may be used.
  • the difference provides a measure of excess homeostatic pressure and is an estimate of the level of fatigue.
  • additional and/or different parameters can be used for the foregoing identification. For example, certain time bins in the lookback window may be assigned greater or less weight to be selected. Including weights can accommodate non-work related constraints or preferences for when workers decide to take proactive discretionary rest. For instance, workers may prioritize sleep during designated times when other activities (e.g., eating, shopping, etc.) may not be possible. People may also use hypnotics or stimulants to promote sleep and wakefulness at specific times.
  • the lookback procedure of process 160 includes recomputing (e.g., using a loop) the homeostatic process at stage 166 over the lookback window by, for instance, invoking the process 150 described above with reference to Figure 4.
  • the future fatigue can be reduced to a value just below the threshold at the operation at stage 126 in Figure 4, e.g., less than 10%, 5%, 1 %, or any other suitable percentages from the threshold, or at least maximally or near-maximally reduced if the discretionary rest period cannot be further increased in duration given available discretionary rest opportunities.
  • Figure 7 is a computing device 300 suitable for executing certain components of the model generator 100 in Figure 1.
  • the computing device 300 can be suitable for the data preprocessor 102, the homeostatic pressure module 104, the predicting engine 106, and/or the lookback module 108 of Figure 1.
  • the computing device 300 can include one or more processors 304 and a system memory 306.
  • a memory bus 308 can be used for communicating between processor 304 and system memory 306.
  • the processor 304 can be of any type including but not limited to a microprocessor (pP), a microcontroller (pC), a digital signal processor (DSP), or any combination thereof.
  • the processor 304 can include one more level of caching, such as a level-one cache 310 and a level-two cache 312, a processor core 314, and registers 316.
  • An example processor core 314 can include an arithmetic logic unit (ALU), a floating-point unit (FPU), a digital signal processing core (DSP Core), a graphical processing unit (GPU), or any combination thereof.
  • An example memory controller 318 can also be used with processor 304, or in some implementations memory controller 318 can be an internal part of processor 304.
  • the system memory 306 can be of any type including but not limited to volatile memory (such as RAM), non-volatile memory (such as ROM, flash memory, etc.) or any combination thereof.
  • the system memory 306 can include an operating system 320, one or more applications 322, such as the model generator 100 of Figure 2, and program data 324, such as the duty schedule 101 and/or the prediction result 103.
  • This described basic configuration 302 is illustrated in Figure 7 by those components within the inner dashed line.
  • the computing device 300 can have additional features or functionality, and additional interfaces to facilitate communications between basic configuration 302 and any other devices and interfaces.
  • a bus/interface controller 330 can be used to facilitate communications between the basic configuration 302 and one or more data storage devices 332 via a storage interface bus 334.
  • the data storage devices 332 can be removable storage devices 336, non-removable storage devices 338, or a combination thereof. Examples of removable storage and non-removable storage devices include magnetic disk devices such as flexible disk drives and hard-disk drives (HDD), optical disk drives such as compact disk (CD) drives or digital versatile disk (DVD) drives, solid state drives (SSD), and tape drives to name a few.
  • HDD hard-disk drives
  • CD compact disk
  • DVD digital versatile disk
  • SSD solid state drives
  • Example computer storage media can include volatile and nonvolatile, removable, and nonremovable media implemented in any method or technology for storage of information, such as computer readable instructions, data structures, program modules, or other data.
  • the system memory 306, removable storage devices 336, and non-removable storage devices 338 are examples of computer readable storage media.
  • Computer readable storage media include, but not limited to, RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM, digital versatile disks (DVD) or other optical storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other media which can be used to store the desired information, and which can be accessed by computing device 300. Any such computer readable storage media can be a part of computing device 300. In other examples, at least one of the foregoing storage devices may be replaced, supplemented, or otherwise linked to a cloud storage device/service via a computer network.
  • the computing device 300 can also include an interface bus 340 for facilitating communication from various interface devices (e.g., output devices 342, peripheral interfaces 344, and communication devices 346) to the basic configuration 302 via bus/interface controller 330.
  • Example output devices 342 include a graphics processing unit 348 and an audio processing unit 350, which can be configured to communicate to various external devices such as a display or speakers via one or more A/V ports 352.
  • Example peripheral interfaces 344 include a serial interface controller 354 or a parallel interface controller 356, which can be configured to communicate with external devices such as input devices (e.g., keyboard, mouse, pen, voice input device, touch input device, etc.) or other peripheral devices (e.g., printer, scanner, etc.) via one or more I/O ports 358.
  • An example communication device 346 includes a network controller 360, which can be arranged to facilitate communications with one or more other computing devices 362 over a network communication link via one or more communication ports 364.
  • the network communication link can be one example of a communication media.
  • Communication media can typically be embodied by computer readable instructions, data structures, program modules, or other data in a modulated data signal, such as a carrier wave or other transport mechanism, and can include any information delivery media.
  • a “modulated data signal” can be a signal that has one or more of its characteristics set or changed in such a manner as to encode information in the signal.
  • communication media can include wired media such as a wired network or direct-wired connection, and wireless media such as acoustic, radio frequency (RF), microwave, infrared (IR) and other wireless media.
  • RF radio frequency
  • IR infrared
  • the term computer readable media as used herein can include both storage media and communication media.
  • the computing device 300 can be implemented as a portion of a small-form factor portable (or mobile) electronic device such as a cell phone, a personal data assistant (PDA), a personal media player device, a wireless web-watch device, a personal headset device, an application specific device, a smart watch, or a hybrid device that include any of the above functions.
  • a small-form factor portable (or mobile) electronic device such as a cell phone, a personal data assistant (PDA), a personal media player device, a wireless web-watch device, a personal headset device, an application specific device, a smart watch, or a hybrid device that include any of the above functions.
  • PDA personal data assistant
  • the computing device 300 can also be implemented as a personal computer including both laptop computer and non-laptop computer configurations.

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Abstract

L'invention décrit des techniques d'amélioration de modélisation informatique pour la génération de prédictions. Dans un exemple, un modèle informatique est généré pour examiner des données représentant des créneaux temporels dans un calendrier. En particulier, le modèle informatique peut être configuré pour calculer une pression homéostatique au niveau d'un premier créneau dans le calendrier et déterminer (1) si la valeur calculée de pression homéostatique au niveau du premier créneau dépasse un seuil prédéfini et (2) si le premier créneau correspond à un statut prédéfini. En réponse à des déterminations positives des deux, le modèle informatique peut rechercher de manière itérative un second créneau dans une partie du calendrier qui précède le premier créneau de telle sorte que des opérations discrétionnaires au niveau du second créneau réduiraient la pression homéostatique au niveau du premier créneau afin qu'elle ne dépasse pas le seuil. Ainsi, le modèle informatique peut prédire les opérations discrétionnaires même sans s'appuyer sur des enregistrements historiques de telles opérations.
PCT/US2024/054864 2023-11-09 2024-11-07 Modélisation informatique de prédictions de repos discrétionnaires Pending WO2025101707A1 (fr)

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Publication number Priority date Publication date Assignee Title
JP2017524426A (ja) * 2014-07-02 2017-08-31 コーニンクレッカ フィリップス エヌ ヴェＫｏｎｉｎｋｌｉｊｋｅ Ｐｈｉｌｉｐｓ Ｎ．Ｖ． 睡眠回復レベルを決定し、表示するシステムおよび方法
KR101925110B1 (ko) * 2018-07-10 2018-12-04 (주)스펙업애드 휴식시간 산정이 가능한 시간표 어플리케이션, 시간표 서버 및 이의 작동 방법
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KR20220067963A (ko) * 2020-11-18 2022-05-25 한국과학기술원 헬스 웨어러블 장치 기반 개인 맞춤형 수면 패턴 제공 프로그램

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* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2017524426A (ja) * 2014-07-02 2017-08-31 コーニンクレッカ フィリップス エヌ ヴェＫｏｎｉｎｋｌｉｊｋｅ Ｐｈｉｌｉｐｓ Ｎ．Ｖ． 睡眠回復レベルを決定し、表示するシステムおよび方法
KR101925110B1 (ko) * 2018-07-10 2018-12-04 (주)스펙업애드 휴식시간 산정이 가능한 시간표 어플리케이션, 시간표 서버 및 이의 작동 방법
US20210090745A1 (en) * 2019-09-20 2021-03-25 Iqvia Inc. Unbiased etl system for timed medical event prediction
US20210282705A1 (en) * 2020-03-16 2021-09-16 Koninklijke Philips N.V. Systems and methods for modeling sleep parameters for a subject
KR20220067963A (ko) * 2020-11-18 2022-05-25 한국과학기술원 헬스 웨어러블 장치 기반 개인 맞춤형 수면 패턴 제공 프로그램

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