Determination of bed-time duration using wearable sensors

• US 10,373,714 B1
• Filed: 01/27/2015
• Issued: 08/06/2019
• Est. Priority Date: 07/12/2013
• Status: Active Grant

• Current Assignee: Vital Connect, Inc.
• Original Assignee: Vital Connect, Inc.
• Inventors: Selvaraj, Nandakumar, Doan, Toai
• Primary Examiner(s): Lee, Paul D
• Application Number:
  US14/606,897
• Time in Patent Office: 1,652 Days
• Field of Search: 702176
• CPC Class Codes: A61B 2562/0219 Inertial sensors, e.g. accelerometers, gyroscopes, tilt switches
• CPC Class Codes: A61B 5/0022 Monitoring a patient using a global network, e.g. telephone networks, internet
• CPC Class Codes: A61B 5/1115 Monitoring leaving of a patient support, e.g. a bed or a wheelchair
• CPC Class Codes: A61B 5/1116 Determining posture transitions
• CPC Class Codes: A61B 5/1118 Determining activity level
• CPC Class Codes: A61B 5/1121 Determining geometric values, e.g. centre of rotation or angular range of movement
• CPC Class Codes: A61B 5/4806 Sleep evaluation A61B5/4821 takes precedence; devices for inducing sleep A61M21/02
• CPC Class Codes: A61B 5/6801 specially adapted to be attached to or worn on the body surface
• CPC Class Codes: A61B 5/6833 Adhesive patches
• CPC Class Codes: A61B 5/7282 Event detection, e.g. detecting unique waveforms indicative of a medical condition A61B5/4094, A61B5/4818, A61B5/349, A61B5/0823 take precedence
• CPC Class Codes: G01B 21/22 for measuring angles or tapers; for testing the alignment of axes
• 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

Abstract

A method and system for automatically determining bed-time periods are disclosed. The method comprises detecting at least one physiological signal, determining a posture angle and an activity level using the at least one detected physiological signal, and determining a bed-time period using both the posture angle and the activity level. The system includes at least one sensor to detect a plurality of physiological signals, a processor coupled to the at least one sensor, and a memory device coupled to the processor, wherein the memory device includes an application that, when executed by the processor, causes the processor to detect a posture angle using at least one of the plurality of detected physiological signals and to determine a bed-time period using both the posture angle and the activity level.

Claims

1. A method for automatically determining bed-time periods, comprising:

detecting, by a wearable sensor device, at least one physiological signal;

determining, by the wearable sensor device, a posture angle and an activity level using the at least one detected physiological signal; and

determining, by the wearable sensor device, a bed-time period using both the posture angle and the activity level, wherein the determining the bed-time period further comprises;

determining a bed-entry event, wherein the determining of the bed-entry event further comprises;

determining whether a fraction of the supine posture over a slower moving window (FSPSMW) is greater than a first threshold, and

in response to the FSPSMW being greater than the first threshold, determining whether a fraction of the supine posture over a faster moving window (FSPFMW) is greater than a second threshold; and

determining a bed-exit event, wherein the bed-time period is a time difference between the bed-exit event and the bed-entry event.

2. The method of claim 1, wherein the at least one detected physiological signal is at least one acceleration signal of a user.

3. The method of claim 2, wherein the determining the posture angle further comprises:

determining whether the posture angle is greater than a supine posture threshold.

4. The method of claim 3, wherein the supine posture threshold is any of a predetermined threshold value and a threshold value learned from a data sampling.

5. The method of claim 4, further comprising:

validating the determination of the bed-time period using the activity level and a minimum bed-time period.

6. The method of claim 2, wherein the activity level is a signal magnitude area (SMA) signal derived from the at least one acceleration signal.

7. The method of claim 1, wherein the FSPSMW is determined as a number of samples with the supine posture divided by a slower moving window of samples.

8. The method of claim 1, wherein the FSPFMW is determined as a number of samples with the supine posture divided by a faster moving window of samples.

9. The method of claim 1, wherein if both the FSPSMW is greater than the first threshold and the FSPFMW is greater than the second threshold, the bed-entry event is determined for a given data sample number.

10. The method of claim 1, wherein the determining of the bed-exit event further comprises:

in response to the FSPSMW being less than or equal to the first threshold, determining whether a fraction of the supine posture over a faster moving window (FSPFMW) is less than the second threshold.

11. The method of claim 10, wherein if the FSPSMW is less than or equal to the first threshold and FSPFMW is less than the second threshold, the bed-exit event is determined as a function of the second threshold and the faster moving window of samples.

12. A system for automatically determining bed-time periods, the system comprising at least one sensor to detect a plurality of physiological signals, a processor coupled to the at least one sensor, and a memory device coupled to the processor, wherein the memory device includes an application that, when executed by the processor, causes the processor to:

determine a posture angle and an activity level using at least one of the plurality of detected physiological signals; and

determine a bed-time period using both the posture angle and the activity level, wherein the determining the bed-time period comprises;

determining a bed-entry event, and

determining a bed-exit event;

wherein the determining the bed-entry event and the bed-exit event comprises;

determining whether a fraction of the supine posture over a slower moving window (FSPSMW) is greater than a first threshold,

in response to the FSPSMW being greater than the first threshold, determining whether a fraction of the supine posture over a faster moving window (FSPFMW) is greater than a second threshold, wherein if both FSPSMW is greater than the first threshold and FSPFMW is greater than the second threshold, the bed-entry event is determined for a given data sample number, and

in response to the FSPSMW being less than or equal to the first threshold, determining whether a fraction of the supine posture over a faster moving window (FSPFMW) is less than the second threshold, wherein if FSPSMW is less than or equal to the first threshold and FSPFMW is less than the second threshold, the bed-exit event is determined for the given data sample number; and

wherein the bed-time period is a time difference between the bed-exit event and the bed-entry event.

13. The system of claim 12, wherein the plurality of physiological signals includes at least one acceleration signal of a user, further wherein the activity level is a signal magnitude area (SMA) signal derived from the at least one acceleration signal.

14. The system of claim 13, wherein to determine the posture angle further comprises to:

determine whether the posture angle is greater than a supine posture threshold.

15. The system of claim 12, wherein the application, when executed by the processor, further causes the processor to:

validate the determination of the bed-time period using the activity level and a minimum bed-time period.