Systems and methods for assessment of fatigue-related contextual performance using historical incident data

• US 8,812,428 B2
• Filed: 09/19/2011
• Issued: 08/19/2014
• Est. Priority Date: 09/20/2010
• Status: Expired due to Fees

• Current Assignee: Pulsar Informatics, Inc.
• Original Assignee: Pulsar Informatics, Inc.
• Inventors: Mollicone, Daniel Joseph, Mott, Christopher Grey
• Primary Examiner(s): Gaffin, Jeffrey A
• Assistant Examiner(s): Bejcek, II, Robert H
• Application Number:
  US13/235,956
• Publication Number:
  US 20120089553A1
• Time in Patent Office: 1,065 Days
• Field of Search: 706/52, 600/300
• US Class Current: 706/52
• CPC Class Codes: G06N 5/048 Fuzzy inferencing
• CPC Class Codes: G06N 7/01 Probabilistic graphical models, e.g. probabilistic networks
• CPC Class Codes: G16H 50/30 for calculating health indices; for individual health risk assessment
• CPC Class Codes: G16H 50/50 for simulation or modelling of medical disorders
• CPC Class Codes: G16Z 99/00 Subject matter not provided for in other main groups of this subclass

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.

Claims

1. A method for assessing the impact of fatigue on performance, the method comprising:

determining an initial model-predicted fatigue level of an individual at an initial time;

creating a mapping function that that transforms a fatigue level to a contextual performance metric, wherein creating the mapping function comprises;

selecting a set of records from a historical fatigue database, the historical fatigue database populated by records, each record comprising;

a contextual performance metric field comprising a contextual performance metric value obtained from one or more historical events; and

a fatigue level field comprising a fatigue level value obtained from one or more individuals associated with the one or more historical events;

grouping the selected records into one or more zones, each zone spanning a range of fatigue levels;

for each zone, combining the contextual performance metric values of the records grouped into the zone to determine a zone-based contextual performance metric value; and

determining the mapping function to be one or more of;

a piecewise function, a discrete function, a continuous function, a linear function, and a look-up table, that correlates fatigue levels within each fatigue zone to a corresponding zone-based contextual performance metric; and

transforming the initial model-predicted fatigue level into an initial contextual performance metric by applying the mapping function to the initial model-predicted fatigue level.

2. A method according to claim 1 further comprising:

determining an additional model-predicted fatigue level of the individual at an additional time, the additional time after the initial time and during a time interval of interest;

transforming the additional model-predicted fatigue levels into an additional contextual performance metric by applying the mapping function to the additional model-predicted fatigue level.

3. A method according to claim 2 wherein determining the additional model-predicted fatigue level comprises applying a fatigue-modeling technique to predict the additional model-predicted fatigue level and wherein applying the fatigue-modeling technique is based at least in part on the initial model-predicted fatigue level.

4. A method according to claim 2 further comprising receiving schedule data for the individual in the time interval of interest, the schedule data comprising at least one of:

activity schedule data relating to times during the time interval of interest that the individual is expected to be performing a particular activity; and

sleep schedule data relating to times during the time interval of interest that the individual is expected to be sleeping.

5. A method according to claim 4 wherein the activity type of an incident comprises one or more of:

operating a vehicle, making goods, testing goods, and performing a military activity.

6. A method according to claim 4 wherein determining the additional model-predicted fatigue level comprises applying a fatigue-modeling technique to estimate the additional model-predicted fatigue level, wherein applying the fatigue-modeling technique is based at least in part on the schedule data.

7. A method according to claim 6 wherein the time interval of interest takes place in the future and the schedule data comprises sleep schedule data which assumes that the individual will be awake during the time interval of interest.

8. A method according to claim 2 further comprising:

receiving a plurality of hypothetical schedule data scenarios, each hypothetical schedule data scenario comprising at least one of;

hypothetical activity schedule data relating to times during the time interval of interest that the individual is expected to be performing a particular activity; and

hypothetical sleep schedule data relating to times during the time interval of interest that the individual is expected to be sleeping;

for each hypothetical schedule data scenario, repeating the steps of;

determining the additional model-predicted fatigue level; and

transforming the additional model-predicted fatigue levels into the additional contextual performance metric;

to thereby obtain, for each hypothetical schedule data scenario, a scenario-specific additional contextual performance metric; and

comparing the scenario-specific additional contextual performance metrics to one another.

9. A method according to claim 8 wherein, for each hypothetical schedule data scenario, determining the additional model-predicted fatigue level comprises applying a fatigue-modeling technique to estimate the additional model-predicted fatigue level and wherein applying the fatigue-modeling technique is based at least in part on the hypothetical schedule data scenario.

10. A method according to claim 2 wherein applying the mapping function to the additional model-predicted fatigue level comprises determining the zone into which the additional model-predicted fatigue-level belongs and outputting, as the additional contextual performance metric, the corresponding zone-based contextual performance metric value.

11. A method according to claim 1 wherein determining the initial model-predicted fatigue level comprises applying a fatigue-measurement technique to obtain a current fatigue level of the individual at a current time.

12. A method according to claim 1 wherein determining the initial model-predicted fatigue level comprises applying a fatigue-modeling technique to estimate the initial model-predicted fatigue level.

13. A method according to claim 1 wherein the contextual performance metric comprises one or more of:

an incident-related metric associated with a particular activity; and

an incident-related metric associated with a particular environment.

14. A method according to claim 13 wherein the environment type of an incident comprises one or more of:

a driver seat of a motor vehicle, a pilot seat of airplane or other air-transport vehicle, a factory setting, a particular stretch of road, a particular segment of a flight, activities occurring at night, the occurrence of activities during the day, the occurrence of activities at dusk, the occurrence of activities at dawn, and the occurrence of activities in a particular geographical region.

15. A method according to claim 1 wherein the contextual performance metric comprises one or more of:

a performance metric associated with a particular activity; and

a performance metric associated with a particular environment.

16. A method according to claim 1 wherein the fatigue level value from the one or more individuals associated with the one or more historical events is obtained by at least one of:

applying a fatigue-measurement technique to the one or more individuals; and

applying a fatigue-modeling technique to the one or more individuals.

17. A method according to claim 1 wherein one or more records populating the historical fatigue database may be created by one or more of:

a manual user entry through an electronic interface, automatic entry through a networked communication interface, and extraction of entries from another database or information system.

18. A method according to claim 1 further comprising:

obtaining the mapping function by selecting the mapping function from among one or more existing mapping functions previously created based on information contained in the historical fatigue database.

19. A method according to claim 1 further comprising:

saving the mapping function into a mapping function database.

20. A method according to claim 1 wherein determining the mapping function comprises using a curve fitting technique to select parameters of a mathematical function such that the fatigue level values of the grouped selected records are fit to the corresponding zone-based contextual performance metric value.

21. A method according to claim 1 wherein grouping the selected records into one or more zones comprises assigning each zone a range of possible fatigue levels such that the differences between an upper and a lower boundary for each range are equal to one another.

22. A method according to claim 1 wherein the range of fatigue levels for one or more of the zones comprises a single value.

23. A method according to claim 1 wherein combining the contextual performance metric values of the records grouped into the zone to determine the zone-based contextual performance metric value comprises averaging the contextual performance metric values of the records grouped into the zone.

24. A method according to claim 1 wherein the contextual performance metric comprises one or more of:

a number of injuries associated with an incident,

a number of injuries associated with an incident occurring in a particular environment,

a number of injuries associated with an incident occurring during the performance of a particular activity,

an injury severity associated with an incident,

an injury severity associated with an incident occurring in a particular environment,

an injury severity associated with an incident occurring during the performance of a particular activity,

a cost of repair associated with an incident,

a cost of repair associated with an incident occurring in a particular environment,

a cost of repair associated with an incident occurring during the performance of a particular activity,

an increase in an insurance premium associated with an incident,

an increase in an insurance premium associated with an incident occurring in a particular environment,

an increase in an insurance premium associated with an incident occurring during the performance of a particular activity,

a performance rate while performing a particular activity,

a performance rate during a work shift,

a performance rate while performing in a particular environment,

an error rate while performing a particular activity,

an error rate during a work shift, and

an error rate while performing in a particular environment.

25. A method according to claim 1 wherein records of the historical fatigue database further comprise one or more classifier fields comprising record-classification criteria associated with the one or more historical events;

and wherein selecting a set of records is based at least in part on record classification criteria in the one or more classifier fields associated with each record.

26. A method according to claim 25 wherein the record-classification criteria of at least one of the one or more classifier fields comprises an environment type of the one or more historical events.

27. A method according to claim 25 wherein the record-classification criteria of at least one of the one or more classifier fields comprises an activity type of the one or more historical events.

28. A method for assessing the impact of fatigue on performance, the method comprising:

receiving schedule data for the individual in a time interval of interest, the schedule data comprising at least one of;

activity schedule data relating to times during the future time interval of interest that the individual is expected to be performing a particular activity; and

sleep schedule data relating to times during the future time interval of interest that the individual is expected to be sleeping;

determining a model-predicted fatigue level of the individual at a predicted time, the predicted time during the time interval of interest;

creating a mapping function that transforms a fatigue level to a contextual performance metric, wherein creating the mapping function comprises;

selecting a set of records from a historical fatigue database, the historical fatigue database populated by records, each record comprising;

a contextual performance metric field comprising a contextual performance metric value obtained from one or more historical events; and

a fatigue level field comprising a fatigue level value obtained from one or more individuals associated with the one or more historical events;

grouping the selected records into one or more zones, each zone spanning a range of possible fatigue levels; and

for each zone, combining the contextual performance metric values of the records grouped into the zone to determine a zone-based contextual performance metric value;

determining the mapping function to be one or more of;

a piecewise function, a discrete function, a continuous function, a linear function, and a look-up table, that correlates fatigue levels within each fatigue zone to a corresponding zone-based contextual performance metric; and

transforming the model-predicted fatigue level into a contextual performance metric by applying the mapping function to the model-predicted fatigue level;

wherein determining the future model-predicted fatigue level is based at least in part on the schedule data.

29. A method for assessing the impact of fatigue on performance, the method comprising:

determining an initial model-predicted fatigue level of an individual at an initial time;

transforming the initial model-predicted fatigue level into an initial contextual performance metric by applying a mapping function to the initial model-predicted fatigue level, the mapping function based at least in part on information contained in an historical fatigue database;

determining an additional model-predicted fatigue level of the individual at an additional time, the additional time after the initial time and during a time interval of interest; and

transforming the additional model-predicted fatigue levels into an additional contextual performance metric by applying the mapping function to the additional model-predicted fatigue level;

wherein the historical fatigue database is populated by records, each record comprising;

a contextual performance metric field comprising a contextual performance metric value obtained from one or more historical events;

a fatigue level field comprising a fatigue level value obtained from one or more individuals associated with the one or more historical events; and

one or more classifier fields comprising record-classification criteria associated with the one or more historical events;

wherein transforming the additional model-predicted fatigue levels into the additional contextual performance metric comprises obtaining the mapping function by creating the mapping function based on information contained in the historical fatigue database;

wherein the mapping function is a discrete mapping function; and

wherein creating the mapping function comprises;

selecting a subset of the historical fatigue database based at least in part on record classification criteria in the one or more classifier fields associated with each record;

grouping the records into a plurality of zones, each zone spanning a range of possible fatigue levels;

for each zone, combining the contextual performance metric values of the records grouped into the zone to determine a zone-based contextual performance metric value; and

determining the mapping function to be one of the zone-based contextual performance metric values which depends on the zone into which the additional model-predicted level belongs.

30. A method according to claim 29 wherein grouping the records into a plurality of zones comprises assigning each zone a range of possible fatigue levels such that the differences between an upper and a lower boundary for each range are equal to one another.

31. A method according to claim 29 wherein grouping the records into a plurality of zones comprises assigning each zone a range of possible fatigue levels such that the union of all zones is contiguous.

32. A method according to claim 31 wherein combining the contextual performance metric values of the records grouped into the zone to determine the zone-based contextual performance metric value comprises averaging the contextual performance metric values of the records grouped into the zone.

33. A method according to claim 32 wherein applying the mapping function to the additional model-predicted fatigue level comprises determining the zone into which the additional model-predicted fatigue-level belongs and outputting, as the additional contextual performance metric, the corresponding zone-based contextual performance metric value.