Situational awareness analysis and fatigue management system

• US 10,354,539 B2
• Filed: 06/08/2015
• Issued: 07/16/2019
• Est. Priority Date: 06/08/2015
• Status: Active Grant

• Current Assignee: BioFli Technologies, Inc.
• Original Assignee: BioFli Technologies, Inc.
• Inventors: Kielbasa, Michael P., Kielbasa, Matthew P., Schlueter, Georg
• Primary Examiner(s): Saint-Vil, Eddy
• Application Number:
  US14/733,446
• Publication Number:
  US 20160358500A1
• Time in Patent Office: 1,499 Days
• CPC Class Codes: G09B 5/02 with visual presentation of the material to be studied, e.g. using film strip

Abstract

A situational awareness analysis and fatigue management system including a processor that receives input data from a user, generates a set of algorithms from the input data, calculates outputs of each of the set of algorithms, and generates and displays a dynamic assessment situational awareness (DASA) diagram of the user as a function of situational awareness performance and wakefulness hours of the user from the calculated output. Using the DASA diagram, the processor identifies situational awareness longevity conditions of the user to perform a task, forecasts advanced fatigue conditions of the user based on the identified situational awareness longevity conditions and identifies improvements of situational awareness performance of the user to perform the task. The processor displays the identified situational awareness longevity conditions, the forecast of advanced fatigue conditions and the improvements of situational awareness performance of the user to perform the task to one or more second users.

Claims

1. A situational awareness analysis and fatigue management system comprising:

a processor;

wherein said processor

receives input data from a user,

displays a series of dynamic psychomotor vigilance tests (D-PVT) to said user requiring said user to respond to stimulus,

accepts successive input data to said series of D-PVT,

calculates a D-PVT performance data, said D-PVT performance data is a difference in time between said successive input data in response to said series of D-PVT as a measure of said user'"'"'s change in response time in responding to said stimulus in milliseconds (msec) for each of said series of D-PVT,

calculates a situational awareness performance data and a wakefulness hours data from said input data and said D-PVT performance data, and

generates a dynamic assessment situational awareness (DASA) diagram of said user as a function of said situational awareness performance data and said wakefulness hours data of said user;

wherein, using said DASA diagram, said processor

receives a task schedule from said user,

identifies situational awareness longevity conditions of said user to perform said task schedule based at least on said difference in time between said successive input data in response to said series of D-PVT,

forecasts advanced fatigue conditions of said user based on said identified situational awareness longevity conditions,

identifies improvements of situational awareness performance of said user to perform said task schedule,

creates a new optimized task schedule based on said task schedule, said situational awareness longevity conditions, said advanced fatigue conditions, and said improvements of situational awareness performance, and,

displays said situational awareness longevity conditions of said user, said advanced fatigue conditions of said user, said improvements of situational awareness performance of said user to perform said task schedule, and said new optimized task schedule to one or more second users.

2. The situational awareness analysis and fatigue management system of claim 1, wherein said input data comprises personal data of said user including height, weight and inseam of said user and a birth year and birth month of said user.

3. The situational awareness analysis and fatigue management system of claim 2, wherein said processor calculates age, body mass index (BMI), and skin-to-mass ratio (SMR) values of said user using said personal data.

4. The situational awareness analysis and fatigue management system of claim 3, wherein said processor calculates a bioelectric impedance (BEI) value and a proportionality factor of said (BEI) as a function of said age, BMI and SMR values of said user.

5. The situational awareness analysis and fatigue management system of claim 1, wherein said processor generates and displays a bar chart comprising said D-PVT measure.

6. The situational awareness analysis and fatigue management system of claim 5, wherein said processor applies linear regression analysis to said bar chart to determine a trend of said user'"'"'s response time as a function of wakefulness hours and display a trend line depicting said trend.

7. The situational awareness analysis and fatigue management system of claim 6, wherein said processor calculates a response time at wake-up (RTW) of said user, wherein said RTW is depicted on said bar chart as said trend line intercepts a y-axis of said bar chart at zero wakefulness hours, and wherein said RTW indicates a user'"'"'s situational awareness.

8. The situational awareness analysis and fatigue management system of claim 6, wherein said processor calculates a response time pitch (RTP) of said user as a change in said user'"'"'s response time per hour of wakefulness, and wherein said RTP indicates said user'"'"'s longevity of effective performance.

9. The situational awareness analysis and fatigue management system of claim 8, wherein said change in said user'"'"'s response time comprises an average rise in said user'"'"'s response time.

10. The situational awareness analysis and fatigue management system of claim 6, wherein said processor

calculates a response time at wake-up (RTW) of said user in milliseconds, wherein said user'"'"'s RTW is depicted on said bar chart as said trend line intercepts a y-axis of said bar chart at zero wakefulness hours, and wherein said user'"'"'s RTW indicates a user'"'"'s situational awareness;

calculates a response time pitch (RTP) of said user as an average rise in said user'"'"'s response time per hour of wakefulness in milliseconds per hour, and wherein said RTP indicates said user'"'"'s longevity of effective performance; and

,

calculates a bio-inertia as a product of said RTW and said RTP.

11. The situational awareness analysis and fatigue management system of claim 10, wherein said processor generates a dynamic psychomotor vigilance test (D-PVT) diagram displaying performance regions and iso-binertia lines of said user using said calculated RTW, RTP and bio-inertia.

12. The situational awareness analysis and fatigue management system of claim 11, wherein said performance regions include

a first performance region below a first pre-determined iso-binertia line, wherein said first performance region indicates a best performance of said user and a best response time of said user;

a second performance region between said first iso-binertia line and a second iso-binertia line, wherein said second performance region indicates a good performance of said user and a good response time of said user; and

,

a third performance region above said second iso-binertia line, wherein said third performance region indicates a poor performance of said user and a poor response time of said user.

13. The situational awareness analysis and fatigue management system of claim 1, wherein said input data comprises sleep behavioral data of said user.

14. The situational awareness analysis and fatigue management system of claim 13, wherein said processor calculates daily sleep deprivation (DSD) and cumulative sleep deprivation (CSD) of said user using said sleep behavioral data.

15. The situational awareness analysis and fatigue management system of claim 13, wherein said processor calculates sleep deprivation of said user from said sleep behavioral data as a difference between 8 hours and actual hours slept.

16. The situational awareness analysis and fatigue management system of claim 1, wherein said input data comprises medication data of said user, wherein said medication data comprises a drowsiness effect of said medication on said user.

17. The situational awareness analysis and fatigue management system of claim 1, wherein said input data comprises performance risk thresholds including blood alcohol content (BAC) thresholds and pre-rapid-eye-movement stage (iREM) thresholds of said user, wherein said iREM depicts wherein optical stimuli of said user are processed with a delay and a long response time or no response time from said user.

18. The situational awareness analysis and fatigue management system of claim 17, wherein said processor

generates a situational awareness scale as a function of situational awareness and wakefulness hours of said user depicting four levels of situational awareness associated with said performance risk thresholds,

wherein said four levels of situational awareness comprise

a low performance risk threshold equivalent to a 0% BAC,

a medium performance risk threshold equivalent to 0.04% BAC,

a high performance risk threshold equivalent to 0.08% BAC, and,

a critical performance risk threshold equivalent to iREM.

19. The situational awareness analysis and fatigue management system of claim 1, wherein said user comprises a driver of an operating equipment.

20. A situational awareness analysis and fatigue management system to optimize a user'"'"'s task schedule comprising:

a processor;

wherein said processor

receives input data from a user, wherein said input data comprises a plurality of groups of input data,

selects a set of functions for each group of said plurality of groups of input data,

calculates outputs of each of said set of functions from said input data, and,

generates and displays to said user a dynamic assessment situational awareness (DASA) diagram of said user as a function of situational awareness performance and wakefulness hours of said user from said output,

displays a series of dynamic psychomotor vigilance tests (D-PVT) to said user requiring said user to respond to stimulus,

accepts successive input data to said series of D-PVT,

calculates a difference in time between said successive input data in response to said series of D-PVT as a measure of said user'"'"'s change in response time in responding to said stimulus in milliseconds (msec) for each of said series of D-PVT;

wherein, using said DASA diagram, said processor

receives a task schedule from said user,

identifies situational awareness longevity conditions of said user to perform said task schedule based at least on said difference in time between said successive input data in response to said series of D-PVT and said input data,

forecasts advanced fatigue conditions of said user based on said identified situational awareness longevity conditions,

identifies improvements of situational awareness performance of said user to perform said task,

creates a new optimized task schedule based on said task schedule, said situational awareness longevity conditions, said advanced fatigue conditions, and said improvements of situational awareness performance, and

displays said identified situational awareness longevity conditions of said user, said forecast of advanced fatigue conditions of said user and said improvements of situational awareness performance of said user to perform said task schedule, and said new optimized task schedule to one or more second users; and

,

wherein said input data comprises

personal data of said user including height, weight and inseam of said user and a birth year and birth month of said user,

wherein said processor calculates age, body mass index (BMI), and skin-to-mass ratio (SMR) values of said user using said personal data, and,

wherein said processor calculates a bioelectric impedance (BEI) value and a proportionality factor of said (BEI) as a function of said age, BMI and SMR values of said user;

sleep behavioral data of said user;

medication data of said user, wherein said medication data comprises a drowsiness effect of said medication on said user; and

,

performance risk thresholds including blood alcohol content (BAC) thresholds and pre-REM stage (iREM) thresholds of said user,

wherein said iREM depicts wherein optical stimuli of said user are processed with a delay and a long response time or no response time from said user.