METHOD AND DEVICE FOR MONITORING POSTURAL AND MOVEMENT BALANCE FOR FALL PREVENTION

• US 20140330171A1
• Filed: 08/26/2013
• Published: 11/06/2014
• Est. Priority Date: 05/03/2013
• Status: Abandoned Application

• Current Assignee: National Taiwan University (Government of The Republic of China), Industrial Technology Research Institute
• Original Assignee: National Taiwan University (Government of The Republic of China), Industrial Technology Research Institute
• Inventors: Pan, Hsin-Hung, Lu, Tung-Wu, Lu, Hsuan-Lun
• Application Number:
  US14/010,189
• Publication Number:
  US 20140330171A1
• Time in Patent Office: Days
• US Class Current: 600/595
• CPC Class Codes: A61B 2562/0219 Inertial sensors, e.g. accelerometers, gyroscopes, tilt switches
• CPC Class Codes: A61B 5/1038 Measuring plantar pressure during gait
• CPC Class Codes: A61B 5/1117 Fall detection
• CPC Class Codes: A61B 5/112 Gait analysis
• CPC Class Codes: A61B 5/1122 of movement trajectories
• CPC Class Codes: A61B 5/1123 Discriminating type of movement, e.g. walking or running A61B5/1116, A61B5/112 take precedence
• CPC Class Codes: A61B 5/726 using Wavelet transforms

Abstract

A method for monitoring postural and movement balance for fall prevent is provided. The method includes the following steps. Multiple sensing signals of a human body are obtained. A center of mass (COM) signal and a center of pressure (COP) signal are modeling according to the sensing signals. A correlation coefficient is calculated according to a mediolateral velocity of the COM signal and the COP signal. A threshold is obtained according to at least one regression model stored in a database. Whether the correlation coefficient is smaller than the threshold is determined. An alert is produced when the correlation coefficient is smaller than the threshold.

Claims

1. A method for monitoring postural and movement balance for fall prevention, comprising:

obtaining a plurality of sensing signals of a human body;

modeling related kinematics of a center of mass (COM) signal and a center of pressure (COP) signal according to the sensing signals;

calculating a correlation coefficient according to a mediolateral velocity of the COM signal and the COP signal;

obtaining a threshold according to at least one regression model stored in a database;

determining whether the correlation coefficient is smaller than the threshold;

outputting an alarm when the correlation coefficient is smaller than the threshold.

2. The method according to claim 1, wherein the sensing signals comprise an inertia sensing signal and a plurality of sole pressure signals.

3. The method according to claim 2, further comprising:

identifying a movement pattern according to the inertia signal;

in the step of obtaining the threshold, selecting the regression model corresponding to the movement pattern from the database according to the movement pattern.

4. The method according to claim 3, wherein the step of identifying the movement pattern comprises:

performing a wavelet transformation on the inertia signal to identify the movement pattern.

5. The method according to claim 4, wherein the movement pattern comprises standing, stepping down, walking, ascending stairs, descending stairs, standing up from sitting, sitting down from standing, and running.

6. The method according to claim 2, wherein the step of modeling related kinematics of the COM signal and the COP signal is performed through calculation by use of an inverted pendulum model.

7. The method according to claim 6, further comprising:

determining a period of single limb support for modeling the inverted pendulum model according to a vertical acceleration of the inertia signal.

8. The method according to claim 1, wherein the at least one regression model represents a relationship between the correlation coefficient in relation to different balance states and COP areas measured during static standings respectively, wherein the COP areas are determined from equivalent areas of COP trajectories.

9. The method according to claim 8, further comprising:

during a static posture, calculating the correlation coefficient and a corresponding COP area according to the sensing signals, and correcting the at least one regression model according to the correlation coefficient and the corresponding COP area.

10. A device for monitoring postural and movement balance for fall prevention, comprising:

a sensing module, for obtaining a plurality of sensing signals of a human body;

a database, for storing at least one regression model; and

a calculation processing module, comprising;

a calculation unit, for modeling related kinematics of a COM signal and a COP signal according to the sensing signals, and calculating a correlation coefficient according to a mediolateral velocity of the COM signal and the COP signal;

a determination unit, for obtaining a threshold according to at least one regression model stored in a database, and determining whether the correlation coefficient is smaller than the threshold;

an output module, for outputting an alarm when the correlation coefficient is smaller than the threshold.

11. The device according to claim 10, wherein the sensing module comprises:

an inertia sensing unit, for obtaining an inertia sensing signal; and

a sole pressure sensing unit, for obtaining a plurality of sole sensing signals.

12. The device according to claim 11, wherein the inertia sensing unit comprises a gyroscope and an accelerometer.

13. The device according to claim 11, wherein the inertia sensing unit is attached near the position of COM on the human body.

14. The device according to claim 11, wherein the sole pressure sensing unit comprises a plurality of pressure sensors disposed on a shoe pad.

15. The device according to claim 14, wherein the pressure sensors are in a number of at least three.

16. The device according to claim 11, further comprising:

a movement identification module, for identifying a movement pattern according to the inertia sensing signal;

wherein, the calculation processing module selects the regression model corresponding to the movement pattern from the database according to the movement pattern.

17. The device according to claim 16, wherein the inertia sensing signal performs a wavelet transformation on the inertia signal to identify the movement pattern.

18. The device according to claim 17, wherein the movement pattern comprises standing, stepping down, walking, ascending stairs, descending stairs, standing up from sitting, sitting down from standing, and running.

19. The device according to claim 11, wherein the calculation processing module models the COM signal and the COP signal according to an inverted pendulum model.

20. The device according to claim 19, wherein the calculation processing module determines a period of single limb support for the inverted pendulum model according to a vertical acceleration of the inertia sensing signal.

21. The device according to claim 10, wherein the at least one regression model represents a relationship between the correlation coefficient in relation to different balance states and COP areas measured during static standings respectively, wherein the COP areas are determined from equivalent areas of COP trajectories.

22. The device according to claim 21, wherein the calculation processing module calculates the correlation coefficient and a corresponding COP area according to the sensing signals during a static posture, and corrects the at least regression model stored in the database according to the corresponding COP area.