System and method for predicting fall risk for a resident

• US 7,612,681 B2
• Filed: 02/06/2007
• Issued: 11/03/2009
• Est. Priority Date: 02/06/2007
• Status: Expired due to Fees

• Current Assignee: General Electric Company
• Original Assignee: General Electric Company
• Inventors: Kornfein, Mark Mitchell, Rajiv, Vrinda, Azzaro, Steven Hector, Sutherland, Hunt Adams
• Primary Examiner(s): PHAM, TOAN NGOC
• Application Number:
  US11/671,770
• Publication Number:
  US 20080186189A1
• Time in Patent Office: 1,001 Days
• Field of Search: 340/573.1, 340/573.7, 340/573.4, 340/686.1, 340/541, 340/551, 340/552, 340/561, 340/565, 340/539.22, 340/539.26, 340/506, 340/825.19, 340/523, 340/526, 340/529, 600/595, 702/141, 702/160
• US Class Current: 340/573.4
• CPC Class Codes: A61B 2503/08 Elderly
• CPC Class Codes: A61B 2505/07 Home care
• CPC Class Codes: A61B 5/1113 Local tracking of patients, e.g. in a hospital or private home
• CPC Class Codes: A61B 5/1117 Fall detection
• CPC Class Codes: A61B 5/112 Gait analysis
• CPC Class Codes: A61B 5/7264 Classification of physiological signals or data, e.g. using neural networks, statistical classifiers, expert systems or fuzzy systems neural networks per se G06N3/00; expert systems per se G06N5/00
• CPC Class Codes: A61B 5/7275 Determining trends in physiological measurement data; Predicting development of a medical condition based on physiological measurements, e.g. determining a risk factor
• CPC Class Codes: G08B 21/043 detecting an emergency event, e.g. a fall
• CPC Class Codes: G08B 21/0469 Presence detectors to detect unsafe condition, e.g. infrared sensor, microphone G08B21/0476 takes precedence
• CPC Class Codes: G16H 15/00 ICT specially adapted for medical reports, e.g. generation or transmission thereof
• CPC Class Codes: G16H 20/10 relating to drugs or medications, e.g. for ensuring correct administration to patients
• CPC Class Codes: G16H 40/63 for local operation
• CPC Class Codes: G16H 50/20 for computer-aided diagnosis, e.g. based on medical expert systems
• CPC Class Codes: G16H 50/30 for calculating health indices; for individual health risk assessment
• CPC Class Codes: G16H 50/70 for mining of medical data, e.g. analysing previous cases of other patients

Abstract

A system for predicting fall risk for a resident is provided. The system includes a fall risk prediction subsystem, configured to predict a fall risk likelihood for a resident based on data measurements collected from one or more sensors. One or more of the sensors comprise range control radar (RCR) sensors. The fall risk prediction subsystem further includes a fall analysis component configured to analyze the data measurements from one or more of the sensors and a fall risk assessment component configured to predict the fall risk likelihood for the resident based on the analysis.

Claims

1. A system for predicting fall risk for a resident, the system comprising:

one or more sensors configured to collect data measurements related to an activity of a resident, wherein one or more of the sensors are configured to measure one or more motion characteristics related to the resident, wherein one or more of the sensors comprise range controlled radar (RCR) sensors, wherein at least one of the RCR sensors measure one or more walking features based on the collected data measurements, wherein one or more of the walking features is selected from a group consisting of;

gait length, variable movement speed, gait/balance instability, transit pauses and drop attacks; and

a fall risk prediction subsystem, configured to predict a fall risk likelihood for the resident based on the data measurements collected from one or more of the sensors.

2. The system of claim 1, wherein the range controlled radar (RCR) sensors comprise one of range gated radar sensors and microwave impulse radar sensors.

3. The system of claim 1, wherein one or more of the sensors further comprises one or more sensors selected from a group consisting of:

motion sensors, photometers, pressure sensors and location sensors positioned within a home of the resident.

4. The system of claim 3, wherein one or more of the sensors are configured to collect data measurements related to at least one of an environmental risk factor and an intrinsic risk factor.

5. The system of claim 4, wherein one or more of the environmental or intrinsic risk factors is related to the resident.

6. The system of claim 4, wherein one or more of the environmental risk factors is selected from a group consisting of:

lighting, support surface structure and obstacles present in a living area of the resident.

7. The system of claim 4, wherein one or more of the intrinsic risk factors is selected from a group consisting of:

cognitive impairment, postural hypotension and visual impairment.

8. The system of claim 1, wherein the fall risk prediction subsystem is configured to analyze the data measurements from one or more of the sensors.

9. The system of claim 8, wherein the fall risk prediction subsystem is further configured to combine the analyzed data measurements with personal data related to the resident to predict the fall risk likelihood for the resident.

10. The system of claim 9, further comprising a database configured to store the analyzed data measurements.

11. The system of claim 9, wherein at least a portion of the personal data is selected from a group consisting of:

fall history, medication and medical conditions related to the resident.

12. The system of claim 9, wherein the fall risk prediction subsystem uses one or more computational and statistical techniques to predict the fall risk likelihood for the resident.

13. The system of claim 1, further comprising a status report generator configured to generate a near real time status report related to the fall risk likelihood of the resident.

14. A system for predicting fall risk for a resident, the system comprising:

a fall risk prediction subsystem, configured to predict a fall risk likelihood for a resident based on data measurements collected from one or more sensors, wherein one or more of the sensors comprise range control radar (RCR) sensors, wherein at least one of the RCR sensors is configured to measure one of gait length, variable movement speed, gait/balance instability, transit pauses and drop attacks, and wherein the fall risk prediction subsystem further comprises;

a fall analysis component configured to analyze the data measurements from one or more of the sensors; and

a fall risk assessment component configured to predict the fall risk likelihood for the resident based on the analysis.

15. The system of claim 14, wherein one or more of the sensors further comprises one or more sensors selected from a group consisting of:

motion sensors, photometers, pressure sensors and location sensors positioned within a home of the resident.

16. The system of claim 15, wherein one or more of the sensors are configured to collect data measurements related to at least one of an environmental risk factor and an intrinsic risk factor.

17. The system of claim 16, wherein one or more of the environmental or intrinsic risk factors is related to the resident.

18. The system of claim 14, wherein at least one of the RCR sensors comprises one of a range gated radar sensor and microwave impulse radar sensor.

19. The system of claim 14, wherein the fall analysis component is configured to analyze one or more of the walking features related to the resident, based on the data measurements.

20. The system of claim 19, wherein the fall risk assessment component is further configured to combine the analyzed data measurements with personal data related to the resident to predict the fall risk likelihood for the resident.

21. The system of claim 20, wherein the fall risk assessment component uses one or more computational and statistical techniques to predict the fall risk likelihood for the resident.

22. The system of claim 14, further comprising a database configured to store the analyzed data measurements.

23. The system of claim 14, further comprising a status report generator configured to generate a near real time status report related to the fall risk likelihood of the resident.

24. A method for predicting fall risk for a resident, the method comprising:

collecting data measurements related to an activity of a resident using one or more sensors positioned within a home of the resident, wherein one or more of the sensors comprises one or more range control radar (RCR) sensors, wherein at least one of the RCR sensors is configured to measure one of gait/balance instability, transit pauses and drop attacks; and

predicting a fall risk likelihood for the resident based on the data measurements collected from one or more of the sensors.

25. The method of claim 24, wherein one or more of the sensors further comprises one or more sensors selected from a group consisting of:

motion sensors, photometers, pressure sensors and location sensors positioned within a home of the resident.

26. The method of claim 25, wherein one or more of the sensors are configured to collect data measurements related to at least one of an environmental risk factor and an intrinsic risk factor.

27. The method of claim 26, wherein one or more of the environmental risk factors is selected from a group consisting of:

lighting, support surface structure and obstacles present in a living area of the resident.

28. The method of claim 26, wherein one or more of the intrinsic risk factors is selected from a group consisting of:

cognitive impairment, postural hypotension and visual impairment.

29. The method of claim 24, wherein at least one of the RCR sensors comprises a microwave impulse radar sensor.

30. The method of claim 24, further comprising analyzing the data measurements to predict the fall risk likelihood for the resident.

31. The method of claim 30, further comprising combining the analyzed data measurements with personal data related to the resident to predict the fall risk likelihood for the resident.

32. The system of claim 31, wherein at least a portion of the personal data is selected from a group consisting of:

fall history, medication and medical conditions related to the resident.

33. The method of claim 31, comprising storing the analyzed data measurements.

34. The method of claim 31, comprising using one or more computational and statistical techniques to predict the fall risk likelihood for the resident. and statistical techniques to predict the fall risk likelihood for the resident.