Human and physical asset movement pattern analyzer

• US 7,978,085 B1
• Filed: 03/02/2009
• Issued: 07/12/2011
• Est. Priority Date: 02/29/2008
• Status: Expired due to Fees

• Current Assignee: Compdata Systems Incorporated, University of South Florida (State University System of Florida)
• Original Assignee: Compdata Systems Incorporated, University of South Florida (State University System of Florida)
• Inventors: Fozard, James L., Kearns, William D., Kostis, Eleftherios
• Primary Examiner(s): Nguyen; Hung T.
• Application Number:
  US12/396,002
• Time in Patent Office: 862 Days
• Field of Search: 340/573.4, 340/573.1, 340/539.12, 340/539.13, 340/572.1, 340/572.4, 340/825.49, 600/300, 600/301, 128/903, 128/920
• US Class Current: 340/573.4
• CPC Class Codes: A61B 5/1113 Local tracking of patients, e.g. in a hospital or private home
• CPC Class Codes: A61B 5/4088 Diagnosing of monitoring cognitive diseases, e.g. Alzheimer, prion diseases or dementia
• CPC Class Codes: G16H 50/20 for computer-aided diagnosis, e.g. based on medical expert systems
• CPC Class Codes: G16Z 99/00 Subject matter not provided for in other main groups of this subclass

Abstract

A system of tracking and analyzing the movement of human subjects. UWB is used to track the location of all persons and/or physical objects (assets) occupying a defined area, giving their location with an accuracy of under twenty centimeters, their acceleration, their path, and a time/date-stamp for each observation. The system then uses artificial intelligence to detect variations in patterns of movement of designated individuals and movable physical objects in the defined space. The extracted patterns inform the design of intelligent methods to automatically track behavioral markers of movement. The system can be used to detect dementia in otherwise healthy human subjects and to identify changes in movement pursuant to medication or health changes and which may precede a fall or to detect proper movement in military training scenarios such as battlefield simulations.

Claims

1. A method of tracking and analyzing movement of human subjects, the method comprising:

providing a plurality of tracking technology sensors;

placing the plurality of sensors within a designated space;

determining an origin location within the space;

providing a plurality of tracking technology transponders, wherein the transponders provide location parameters;

affixing one of the plurality of transponders to each human subject;

collecting the location parameters of each human subject from the transponders at predetermined time intervals using the sensors;

determining a plurality of movement paths for each human subject from the location parameters, wherein each movement path comprises the location parameters from each period of movement of each human subject;

recording the plurality of movement paths for fractal dimension tortuosity characteristics;

setting a spatial scale;

calculating an estimated fractal dimension for each of the plurality of movement paths of each human subject; and

calculating an average of the estimated fractal dimensions for each of the plurality of movement paths for each subject to produce a single fractal dimension score for each human subject, wherein a high fractal dimension score corresponds to a high likelihood of agitated movement and the greater the value of the fractal dimension score corresponds to the greater degree of agitated movement.

2. The method of claim 1, further comprising:

removing location parameters that represent impossible movement.

3. The method of claim 1, further comprising:

removing location parameters that represent movement outside the designated space.

4. The method of claim 1, further comprising:

removing location parameters that represent non-movement for a predetermined time.

5. The method of claim 1, further comprising:

correlating the fractal dimension tortuosity characteristics and cognitive status of the human subjects.

15. The method of claim 1, wherein the tracking technology is chosen from the group consisting of ultra-wideband radio technology and radio-frequency identification (RFID) technology.

16. The method of claim 1, wherein the transponders provide location parameters in three planes (x, y, and z).

6. A method of detecting dementia in human subjects, the method comprising:

providing a plurality of tracking technology sensors;

placing the plurality of sensors within a designated space such that the daily movements of the subjects are captured by the sensors;

determining an origin location within the space;

providing a plurality of tracking technology transponders, wherein the transponders provide location parameters;

affixing one of the plurality of transponders to each human subject;

collecting the location parameters of each human subject from the transponders at predetermined time intervals using the sensors;

determining a plurality of movement paths for each human subject from the location parameters, wherein each movement path comprises the location parameters from each period of movement of each subject;

analyzing the plurality of movement paths for fractal dimension tortuosity characteristics;

choosing the appropriate spatial scale;

estimating Fractal D fractal dimension for each of the plurality of movement paths of each human subject; and

calculating an average of the estimated fractal dimensions for each of the plurality of movement paths for each human subject to produce a single fractal dimension score for each human subject, wherein a high fractal dimension score corresponds to a high likelihood of dementia and the greater the value of the fractal dimension score corresponds to the greater degree of dementia.

7. The method of claim 6, wherein an accuracy of the location parameters is within about 20 cm.

8. The method of claim 6, wherein collecting the location parameters occurs about every 0.4 seconds.

9. The method of claim 6, further comprising:

correlating the fractal dimension tortuosity characteristics and cognitive status of the human subjects.

10. The method of claim 6, further comprising:

removing location parameters that represent impossible movement.

11. The method of claim 6, further comprising:

removing location parameters that represent movement outside the designated space.

12. The method of claim 6, further comprising:

removing location parameters that represent non-movement for a predetermined time.

13. The method of claim 6, wherein the tracking technology is chosen from the group consisting of ultra-wideband radio technology and radio-frequency identification (RFID) technology.

14. The method of claim 6, wherein the transponders provide location parameters in three planes (x, y, and z).