Methods and apparatus for measuring physiological parameters

• US 20160007935A1
• Filed: 09/22/2015
• Published: 01/14/2016
• Est. Priority Date: 03/19/2014
• Status: Active Application

• Current Assignee: Massachusetts Institute of Technology
• Original Assignee: Massachusetts Institute of Technology
• Inventors: Hernandez, Javier, McDuff, Daniel, Picard, Rosalind
• Application Number:
  US14/861,388
• Publication Number:
  US 20160007935A1
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: A61B 2562/0219 Inertial sensors, e.g. accelerometers, gyroscopes, tilt switches
• CPC Class Codes: A61B 5/0077 Devices for viewing the surface of the body, e.g. camera, magnifying lens
• CPC Class Codes: A61B 5/0205 Simultaneously evaluating both cardiovascular conditions and different types of body conditions, e.g. heart and respiratory condition
• CPC Class Codes: A61B 5/024 Detecting, measuring or recording pulse rate or heart rate A61B5/0205, A61B5/021 take precedence
• CPC Class Codes: A61B 5/02405 Determining heart rate variability
• CPC Class Codes: A61B 5/02416 using photoplethysmograph signals, e.g. generated by infrared radiation A61B5/14552 takes precedence
• CPC Class Codes: A61B 5/02438 with portable devices, e.g. worn by the patient
• CPC Class Codes: A61B 5/0816 Measuring devices for examining respiratory frequency measuring frequency of electric signals G01R23/00
• CPC Class Codes: A61B 5/1116 Determining posture transitions
• CPC Class Codes: A61B 5/1118 Determining activity level
• 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 5/113 occurring during breathing
• CPC Class Codes: A61B 5/117 Identification of persons methods or arrangements for recognising patterns, e.g. fingerprints, G06F18/00, G06V40/00; identification of persons by analysing their voice or speech G10L17/00
• CPC Class Codes: A61B 5/6814 Head
• CPC Class Codes: A61B 5/6824 Arm or wrist
• CPC Class Codes: A61B 5/7278 Artificial waveform generation or derivation, e.g. synthesising signals from measured signals A61B5/327 takes precedence

Abstract

A sensor system includes one or more gyroscopes and one or more accelerometers, for measuring subtle motions of a user'"'"'s body. The system estimates physiological parameters of a user, such as heart rate, breathing rate and heart rate variability. When making the estimates, different weights are assigned to data from different sensors. For at least one estimate, weight assigned to data from at least one gyroscope is different than weight assigned to data from at least one accelerometer. Also, for at least one estimate, a weight assigned to one or more sensors located in a first region relative to the user'"'"'s body is different than a weight assigned to one or more sensors located in a second region relative to the user'"'"'s body. Furthermore, weight assigned to data from at least one sensor changes over time.

Claims

1. A sensor system that comprises a set of sensors for measuring motion of a user'"'"'s body, which set of sensors includes one or more gyroscopes and one or more accelerometers, wherein the sensor system is configured:

(a) to make estimations of one or more physiological parameters of a user, based on data from the set of sensors,

(b) to assign different weights to data from different sensors when making the estimations, such that

(i) for at least one estimation, a weight assigned to data from at least one gyroscope is different than a weight assigned to data from at least one accelerometer;

(ii) for at least one estimation, a weight assigned to data from a first sensor located in a first region relative to the user'"'"'s body is different than a weight assigned to data from a second sensor located in a second region relative to the user'"'"'s body, which first and second regions do not intersect, the first and second sensors being a single type of motion sensor; and

(iii) a weight assigned to data from at least one sensor changes from at least one estimation to another estimation.

2. The sensor system of claim 1, wherein a weight assigned to a sensor depends at least in part on whether a user is standing, sitting or lying down.

3. The sensor system of claim 1, wherein a weight assigned to data from a specific sensor depends at least in part on periodicity of a signal measured by the specific sensor.

4. The sensor system of claim 1, wherein a weight assigned to data from a given sensor depends at least in part on magnitude of the highest magnitude frequency component of the data from the given sensor.

5. The sensor system of claim 1, wherein a weight assigned to data from a sensor depends at least in part on identity of the user.

6. The sensor system of claim 1, wherein a weight assigned to data from a sensor depends at least in part on physiological gender of the user.

7. The sensor system of claim 1, wherein a weight assigned to data from a sensor depends at least in part on age of the user.

8. The sensor system of claim 1, wherein a specific weight assigned to data from a sensor depends at least in part on what is being calculated, in a calculation that involves a multiplication of a term by the given weight.

9. The sensor system of claim 1, wherein a weight assigned to data from a particular sensor depends at least in part on magnitude of linear acceleration measured by the particular sensor.

10. The sensor system of claim 1, wherein the first and second regions are selected from a set of regions that includes (a) a region adjacent to the user'"'"'s head, and (b) a region that is adjacent to the user'"'"'s wrist and that does not intersect the region adjacent to the user'"'"'s head.

11. The sensor system of claim 1, wherein the one or more physiological parameters include cardiac pulse rate.

12. The sensor system of claim 1, wherein the one or more physiological parameters include respiratory rate.

13. The sensor system of claim 1, wherein the one or more physiological parameters include heart rate variability.

14. The sensor system of claim 1, wherein the sensor system is configured:

(a) to make a biometric identification of the identity of the user, based at least in part on measurements taken by the one or more accelerometers and one or more gyroscopes; and

(b) to assign different weights to data from different sensors, when making the biometric identification.

15. The sensor system of claim 14, wherein the sensor system is configured to assign different weights to data from different sensors, such that, when making the biometric identification, a weight assigned to data from at least one gyroscope is different than a weight assigned to data from at least one accelerometer.

16. The sensor system of claim 14, wherein the sensor system is configured to assign different weights to data from different sensors, such that, when making the biometric identification, a weight assigned to data from a sensor (Sensor A) located in a first region relative to the user'"'"'s body is different than a weight assigned to data from a sensor (Sensor B) located in a second region relative to the user'"'"'s body, which first and second regions do not intersect, Sensor A and Sensor B being a single type of motion sensor.

17. The sensor system of claim 1, wherein:

(a) the sensor system includes one or more optical sensors for measuring light that reflects from or is transmitted through skin; and

(b) the sensor system is configured to assign different weights to data from different sensors when making the estimations, such that for at least one estimation, a weight assigned to data from at least one optical sensor is different than a weight assigned to data from at least one accelerometer or from at least one gyroscope.

18. The sensor system of claim 17, wherein at least one optical sensor is a photoplethysmographic sensor.

19. The sensor system of claim 17, wherein at least one optical sensor is a camera that measures motion of a scene relative to the user.

20. A method comprising, in combination:

(a) a set of sensors measuring motion of a user'"'"'s body, which set of sensors includes one or more gyroscopes and one or more accelerometers; and

(b) one or more computers making estimations of one or more physiological parameters of a user, based on data from the set of sensors, such that

(i) for at least one estimation, a weight assigned to data from at least one gyroscope is different than a weight assigned to data from at least one accelerometer; and

(ii) for at least one estimation, a weight assigned to data from a first sensor located in a first region relative to the user'"'"'s body is different than a weight assigned to data from a second sensor located in a second region relative to the user'"'"'s body, which first and second regions do not intersect, the first and second sensors being a single type of motion sensor; and

(iii) a weight assigned to data from at least one sensor changes over time.