Hierarchical workload monitoring for optimal subordinate tasking

• US 7,454,313 B2
• Filed: 05/30/2006
• Issued: 11/18/2008
• Est. Priority Date: 05/30/2006
• Status: Expired due to Fees

• Current Assignee: Honeywell International Inc.
• Original Assignee: Honeywell International Inc.
• Inventors: Mathan, Santosh, Ververs, Patricia M., Dorneich, Michael C., Whitlow, Stephen D.
• Primary Examiner(s): Lau; Tung S
• Assistant Examiner(s): Kundu; Sujoy K
• Application Number:
  US11/442,894
• Publication Number:
  US 20070282566A1
• Time in Patent Office: 903 Days
• Field of Search: 702/182, 702/97, 702/127, 702141-142, 702/150, 702/153, 482 1- 9, 482900-902, 701/207, 701213-216
• US Class Current: 702/182
• CPC Class Codes: G06Q 10/06 Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
• CPC Class Codes: Y10S 482/90 Ergometer with feedback to load or with feedback comparison
• CPC Class Codes: Y10S 482/901 having computer circuitry
• CPC Class Codes: Y10S 482/902 Employing specific graphic or video display

Abstract

A system and method of hierarchical monitoring for subordinate assessment is described. Generally, the system receives remotely sensed data about a user'"'"'s physiology and contextual state, and extracts features that are related to the workload currently being experienced by a single subordinate and/or group of subordinates. The system builds workload indices based on levels of these features. The system uses statistical process control techniques in conjunction with the workload indices to identify suboptimal user states. Information regarding a particular subordinate or the group of subordinates may be presented in a variety of different ways. As a result, a superior can easily determine the condition of the one or more subordinates and make task adjustments to influence workload levels.

Claims

1. A method for analyzing subordinate workload, comprising in combination:

receiving data from at least one sensor that detects at least one physiological characteristic, at least one contextual characteristic, and at least one cognitive state of an individual;

selecting data that are relevant to a workload level of the individual;

normalizing the selected data to build workload indices;

comparing the workload indices with historical data to evaluate a current workload state of the individual; and

displaying the current workload state of the individual.

2. The method of claim 1, wherein the at least one sensor is selected from the group consisting of one or more of an electroencephalogram (EEG) sensor, an electrocardiogram (ECG) sensor, an electro-oculogram (EOG) sensor, an impedance pneumogram (ZPG) sensor, a galvanic skin response (GSR) sensor, a blood volume pulse (BVP) sensor, a pupilometry sensor, visual scanning sensor, blood oxygenation sensor, a respiration sensor, an electromyogram (EMG), a blood pressure sensor, a skin and core body temperature sensor, a near-infrared optical brain imaging sensor, an accelerometer, an acoustic sensor, a global positioning system (GPS), and a gyroscope.

3. The method of claim 1, wherein the at least one sensor is located on at least one of the individual'"'"'s body, clothing, and gear.

4. The method of claim 1, wherein the at least one sensor is located near the individual.

5. The method of claim 1, wherein selecting the data includes identifying what data are relevant to the workload level prior to receiving the data.

6. The method of claim 1, wherein presenting the normalized data includes presenting the normalized data in relationship to a historical data distribution.

7. The method of claim 6, wherein the normalized data are presented as a z-score that is normalized to the historical data distribution.

8. The method of claim 1, wherein displaying the normalized data includes displaying at least a portion of the normalized data on a coincident factor display.

9. The method of claim 1, wherein presenting the normalized data includes presenting a normalized historical trend chaff.

10. The method of claim 1, further comprising adjusting the workload level of the individual if the workload level is not within a predetermined range.

11. A system for analyzing subordinate workload, comprising in combination:

a processor;

data storage; and

machine language instructions stored in the data storage executable by the processor to;

receive data from at least one sensor that detects at least one physiological characteristic, at least one contextual characteristic, and at least one cognitive state of an individual;

extract features from the data that are relevant to a workload level of the individual;

normalize the data of the extracted features to build workload indices;

compare the workload indices to expected workload levels; and

identify whether the workload level is within a predetermined range.

12. The system of claim 11, wherein extracting features that are relevant to the workload level includes selecting what data to use to build the workload indices.

13. The system of claim 11, wherein building the workload indices includes normalizing the extracted features.

14. The system of claim 11, further comprising machine language instructions stored in the data storage executable by the processor to display the workload level.

16. The system of claim 14, wherein the display means presents the normalized data in relationship to a historical data distribution.

17. The system of claim 14, wherein the display means presents the normalized data on a coincident factor display.

18. The system of claim 14, wherein the display means presents the normalized data in a normalized history view.

15. A system for analyzing subordinate workload, comprising in combination:

data logging means operable to receive, store, and index data representative of at least one physical characteristic, at least one contextual characteristic, and at least one cognitive state of an individual;

distribution means operable to select data from the indexed data relevant to a workload level of an individual and normalize the selected data;

statistical process control means operable to calculate a mean and standard deviation associated with the normalized data; and

display means operable to present the normalized data in a context to evaluate the workload level of the individual.