Calibration of a chest-mounted wireless sensor device for posture and activity detection

• US 9,632,981 B2
• Filed: 05/22/2013
• Issued: 04/25/2017
• Est. Priority Date: 07/12/2012
• Status: Active Grant

• Current Assignee: Vital Connect, Inc.
• Original Assignee: Vital Connect, Inc.
• Inventors: Chan, Alexander, Ferdosi, Nima, Narasimhan, Ravi
• Primary Examiner(s): Bui, Bryan
• Application Number:
  US13/900,438
• Publication Number:
  US 20140019080A1
• Time in Patent Office: 1,434 Days
• Field of Search: 702 85
• CPC Class Codes: A61B 2560/0223 of calibration, e.g. protocols for calibrating sensors
• CPC Class Codes: A61B 2562/0219 Inertial sensors, e.g. accelerometers, gyroscopes, tilt switches
• CPC Class Codes: A61B 2562/0247 Pressure sensors
• CPC Class Codes: A61B 5/00 Measuring for diagnostic purposes radiation diagnosis A61B6/00; diagnosis by ultrasonic, sonic or infrasonic waves A61B8/00; Identification of persons
• CPC Class Codes: A61B 5/0002 Remote monitoring of patients using telemetry, e.g. transmission of vital signals via a communication network A61B5/07 takes precedence; transmission systems for measured values G08C; transmission H04B; transmission of digital information H04L; wireless communication networks H04W
• CPC Class Codes: A61B 5/11 Measuring movement of the entire body or parts thereof, e.g. head or hand tremor, mobility of a limb for measuring pulse A61B5/02 ;A61B5/1038 takes precedence; motion detection to correct for motion artifacts in physiological signals A61B5/721
• 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 5/4809 Sleep detection, i.e. determining whether a subject is asleep or not
• CPC Class Codes: G01D 21/00 Measuring or testing not otherwise provided for
• CPC Class Codes: G01P 15/00 Measuring acceleration; Measuring deceleration; Measuring shock, i.e. sudden change of acceleration
• CPC Class Codes: G01P 15/18 in two or more dimensions
• CPC Class Codes: G01P 21/00 Testing or calibrating of apparatus or devices covered by the preceding groups
• CPC Class Codes: G04F 13/04 using electrochemical means
• CPC Class Codes: G06F 17/00 Digital computing or data processing equipment or methods, specially adapted for specific functions information retrieval, database structures or file system structures therefor G06F16/00
• CPC Class Codes: G06F 17/40 Data acquisition and logging for input to computer G06F3/00
• CPC Class Codes: G07C 1/00 Registering, indicating or recording the time of events or elapsed time, e.g. time-recorders for work people registering or indicating the working of machines or vehicles G07C3/00, G07C5/00
• CPC Class Codes: G16Z 99/00 Subject matter not provided for in other main groups of this subclass

Abstract

A method and system for calibrating a wireless sensor device are disclosed. In a first aspect, the method comprises determining a vertical calibration vector and determining a rotation matrix using the vertical calibration vector to line up native axes of the wireless sensor device with body axes. In a second aspect, a wireless sensor device comprises a processor 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 vertical calibration vector and to determine a rotation matrix using the vertical calibration vector to line up native axes of the wireless sensor device with body axes.

Claims

1. A method for calibrating a wireless sensor device, the method comprising:

detecting accelerometer data in 3 axes within a predetermined time period by the wireless sensor device;

determining an acceleration vector corresponding to a footstep number;

determining a vertical calibration vector using the acceleration vector and a walking detection algorithm, wherein the walking detection algorithm;

calculates a signal magnitude area (SMA) for the predetermined time period using the accelerometer data,

calculates a magnitude of acceleration in a horizontal plane and an overall magnitude of acceleration, and

compares the SMA to a first threshold and the overall magnitude of acceleration to both a second threshold and a third threshold for a result of the walking detection algorithm; and

determining a rotation matrix using the determined vertical calibration vector to line up native axes of the wireless sensor device with body axes.

2. The method of claim 1, wherein the determining the vertical calibration vector, when executed by a processor via the application, further comprises:

determining the vertical calibration vector using at least one of;

automatic calibration, adaptive calibration, or manual calibration.

3. The method of claim 2, wherein the determining the vertical calibration vector using the automatic calibration comprises:

determining the acceleration vector corresponding to the footstep number of a user of the wireless sensor device; and

determining whether the footstep number meets a minimum footstep number threshold within the predetermined time period.

4. The method of claim 2, wherein the determining the vertical calibration vector using the adaptive calibration comprises:

detecting an inaccuracy with the automatic calibration; and

adjusting the vertical calibration vector slowly over time.

5. The method of claim 4, wherein the adjusting the vertical calibration vector slowly over time comprises:

adding current acceleration samples during a detected walking period; and

normalizing the vertical calibration vector to provide an adjusted calibration vector.

6. The method of claim 2, wherein the determining the vertical calibration vector using the manual calibration, when executed by the processor via the application, comprises:

determining the vertical calibration vector using at least one of;

upright manual calibration, manual calibration based on walking, or bedridden manual calibration.

7. The method of claim 6, wherein the determining the vertical calibration vector using the upright manual calibration comprises:

receiving an upright manual calibration request from a user in a standing upright position via a relay;

detecting a MEMS based vertical calibration vector;

filtering the MEMS based vertical calibration vector using a lowpass filter;

replacing a current upright vertical calibration vector with the filtered MEMS based vertical calibration vector; and

transmitting a status message to the relay.

8. The method of claim 6, wherein the determining the vertical calibration vector using the manual calibration based on walking comprises:

receiving a manual calibration request from a user during a walking period via a relay;

calculating a MEMS based vertical calibration vector during the walking period;

determining whether the walking period meets another minimum footstep number threshold; and

transmitting a status message to the relay.

9. The method of claim 6, wherein the determining the vertical calibration vector using the bedridden manual calibration comprises:

receiving a manual calibration request from a bedridden user via a relay;

determining both a supine calibration vector and a leaning calibration vector;

calculating a MEMS based vertical calibration vector based on the supine calibration vector and the leaning calibration vector; and

transmitting a status message to the relay.

10. The method of claim 6, wherein the manual calibration based on walking, when executed by the processor via the application, further causes the processor to:

receive a manual calibration request from a user during a walking period via a relay;

calculate a MEMS based vertical calibration vector during the walking period;

determine whether the walking period meets another minimum footstep number threshold; and

transmit a status message to the relay.

11. A wireless sensor device, comprising:

a processor; 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 accelerometer data in 3 axes within a predetermined time period by the wireless sensor device;

determine an acceleration vector corresponding to a footstep number;

determine a vertical calibration vector using the acceleration vector and a walking detection algorithm, wherein the walking detection algorithm;

calculates a signal magnitude area (SMA) for the predetermined time period using the accelerometer data,

calculates a magnitude of acceleration in a horizontal plane and an overall magnitude of acceleration, and

compares the SMA to a first threshold and the overall magnitude of acceleration to both a second threshold and a third threshold for a result of the walking detection algorithm; and

determine a rotation matrix using the determined vertical calibration vector to line up native axes of the wireless sensor device with body axes.

12. The device of claim 11, wherein the determine the vertical calibration vector comprises:

determine the vertical calibration vector using at least one of;

automatic calibration, adaptive calibration, or manual calibration.

13. The device of claim 12, wherein the determine the vertical calibration vector using the automatic calibration comprises:

determine the acceleration vector corresponding to the footstep number of a user of the wireless sensor device; and

determine whether the footstep number meets a minimum footstep number threshold within the predetermined time period.

14. The device of claim 12, wherein the determine the vertical calibration vector using the adaptive calibration comprises:

detect an inaccuracy with the automatic calibration; and

adjust the vertical calibration vector slowly over time.

15. The device of claim 14, wherein the adjust the vertical calibration vector slowly over time comprises:

add current acceleration samples during a detected walking period; and

normalize the vertical calibration vector to provide an adjusted calibration vector.

16. The device of claim 12, wherein the determine the vertical calibration vector using the manual calibration comprises:

determine the vertical calibration vector using at least one of;

upright manual calibration, manual calibration based on walking, or bedridden manual calibration.

17. The device of claim 16, wherein the upright manual calibration, when executed by the processor via the application, further causes the processor to:

receive an upright manual calibration request from a user in a standing upright position via a relay;

detect a MEMS based vertical calibration vector;

filter the MEMS based vertical calibration vector using a lowpass filter;

replace a current upright vertical calibration vector with the filtered MEMS based vertical calibration vector; and

transmit a status message to the relay.

18. The device of claim 16, wherein the bedridden manual calibration, when executed by the processor via the application, further causes the processor to:

receive a manual calibration request from a bedridden user via a relay;

determine both a supine calibration vector and a leaning calibration vector;

calculate a MEMS based vertical calibration vector based on the supine calibration vector and the leaning calibration vector; and

transmit a status message to the relay.