Enhanced Real Time Frailty Assessment for Mobile

• US 20160166180A1
• Filed: 11/04/2015
• Published: 06/16/2016
• Est. Priority Date: 12/11/2014
• Status: Abandoned Application

• Current Assignee: Precise Mobile Technologies LLC
• Original Assignee: Precise Mobile Technologies LLC
• Inventors: Martin, David
• Application Number:
  US14/932,591
• Publication Number:
  US 20160166180A1
• Time in Patent Office: Days
• CPC Class Codes: A61B 2560/0209 adapted for power saving
• CPC Class Codes: A61B 5/1112 Global tracking of patients, e.g. by using GPS
• CPC Class Codes: A61B 5/112 Gait analysis
• CPC Class Codes: A61B 5/4023 Evaluating sense of balance
• CPC Class Codes: A61B 5/4866 Evaluating metabolism A61B5/083 takes precedence
• CPC Class Codes: A61B 5/6803 Head-worn items, e.g. helmets, masks, headphones or goggles
• CPC Class Codes: A61B 5/6898 Portable consumer electronic devices, e.g. music players, telephones, tablet computers
• CPC Class Codes: G01C 22/006 Pedometers

Abstract

Some embodiments of the invention provide methods and apparatus for enhanced real time frailty assessment leveraging a mobile or wearable device. In some embodiments, the mobile or wearable device user'"'"'s gait characteristics are determined in real time, and said information is integrated with additional information comprising the user'"'"'s balance evaluation and contextual information obtained making use of the mobile or wearable device sensors, in order to deliver an enhanced frailty assessment.

Claims

1. A method for real time monitoring of a mobile and/or wearable device user, the method comprising:

obtaining sensors data, where sensors comprise accelerometer;

obtaining the squares of wavelet transformation coefficients of accelerometer data;

weighting the squares of wavelet transformation coefficients;

obtaining the summation of said weighted squares, and leverage said summation to estimate the velocity of the user;

2. The method of claim 1, further comprising:

obtaining an indication of the user'"'"'s balance using the mobile or wearable device sensors data.

3. The method of claim 2, further comprising:

obtaining location information leveraging sensors data;

combining user'"'"'s velocity with location information for enhanced localization and calibration.

4. The method of claim 3, further comprising:

leveraging weighted energies of the accelerometer wavelet transformation coefficients to choose the coefficients from which to obtain a reconstructed wave from where each step of the user is clearly identified;

combining step time information with velocity estimation to estimate step length.

5. The method of claim 2, wherein obtaining the balance indication comprises wavelet de-noising and Kalman filtering with the sensors data.

6. The method of claim 1, further comprising:

leveraging said velocity to estimate calories burned per time unit.

7. The method of claim 6, further comprising:

displaying in real time in the device screen the determined calories burned per time unit.

8. A method for real time monitoring of a mobile or wearable device user, the method comprising:

obtaining sensors data;

obtaining the wavelet transformation coefficients of sensors data;

obtaining an indication of the user'"'"'s balance using wavelet de-noising on the sensors data and Kalman filtering with said de-noised data.

9. A system comprising:

a processor;

a non-transitory processor-readable medium including one or more instructions which, when executed by the processor, causes the processor to monitor a mobile and/or wearable device user in real time by;

obtaining sensors data, where sensors comprise accelerometer;

obtaining the squares of wavelet transformation coefficients of accelerometer data;

weighting the squares of wavelet transformation coefficients;

obtaining the summation of said weighted squares, and leverage said summation to estimate the velocity of the user.

10. The system of claim 9, wherein the monitoring a mobile and/or wearable device user in real time, further comprises:

obtaining an indication of the user'"'"'s balance using the mobile or wearable device sensors data.

11. The system of claim 10, wherein the monitoring a mobile and/or wearable device user in real time, further comprises:

obtaining location information leveraging sensors data;

combining user'"'"'s velocity with location information for enhanced localization and calibration.

12. The system of claim 11, wherein the monitoring a mobile and/or wearable device user in real time, further comprises:

leveraging weighted energies of the accelerometer wavelet transformation coefficients to choose the coefficients from which to obtain a reconstructed wave from where each step of the user is clearly identified;

combining step time information with velocity estimation to estimate step length.

13. The system of claim 10, wherein obtaining the balance indication comprises wavelet de-noising and Kalman filtering with the sensors data.

14. The system of claim 9, wherein the monitoring a mobile and/or wearable device user in real time, further comprises:

leveraging said velocity to estimate calories burned per time unit.

15. The system of claim 14, wherein the monitoring a mobile and/or wearable device user in real time, further comprises:

displaying in real time in the device screen the determined calories burned per time unit.