Respiration-based estimation of an aerobic activity parameter

• US 20190046044A1
• Filed: 10/10/2018
• Published: 02/14/2019
• Est. Priority Date: 06/14/2015
• Status: Active Grant

• Current Assignee: Facense Ltd.
• Original Assignee: Facense Ltd.
• Inventors: Tzvieli, Arie, Thieberger, Gil, Frank, Ari M
• Granted Patent: US 10,524,667 B2
• Time in Patent Office: Days
• CPC Class Codes: A61B 2503/12 Healthy persons not otherwise provided for, e.g. subjects of a marketing survey
• CPC Class Codes: A61B 2505/07 Home care
• CPC Class Codes: A61B 5/0077 Devices for viewing the surface of the body, e.g. camera, magnifying lens
• CPC Class Codes: A61B 5/015 By temperature mapping of body part
• 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 5/02055 Simultaneously evaluating both cardiovascular condition and temperature
• CPC Class Codes: A61B 5/0295 using plethysmography, i.e. measuring the variations in the volume of a body part as modified by the circulation of blood therethrough, e.g. impedance plethysmography
• CPC Class Codes: A61B 5/0816 Measuring devices for examining respiratory frequency measuring frequency of electric signals G01R23/00
• CPC Class Codes: A61B 5/0833 Measuring rate of oxygen consumption
• CPC Class Codes: A61B 5/0836 Measuring rate of CO2 production
• CPC Class Codes: A61B 5/1455 using optical sensors, e.g. spectral photometrical oximeters
• CPC Class Codes: A61B 5/14552 Details of sensors specially adapted therefor
• CPC Class Codes: A61B 5/318 Heart-related electrical modalities, e.g. electrocardiography [ECG]
• CPC Class Codes: A61B 5/6803 Head-worn items, e.g. helmets, masks, headphones or goggles
• CPC Class Codes: A61B 5/681 Wristwatch-type devices
• CPC Class Codes: A61B 5/6814 Head
• CPC Class Codes: A61B 5/6819 Nose
• CPC Class Codes: A61B 5/6835 Supports or holders, e.g., articulated arms
• CPC Class Codes: A61B 5/7264 Classification of physiological signals or data, e.g. using neural networks, statistical classifiers, expert systems or fuzzy systems neural networks per se G06N3/00; expert systems per se G06N5/00
• CPC Class Codes: G02B 2027/0138 comprising image capture systems, e.g. camera
• CPC Class Codes: G02B 2027/014 : comprising information/image processing systems
• CPC Class Codes: G02B 27/017 : Head mounted
• CPC Class Codes: G16H 50/20 : for computer-aided diagnosis, e.g. based on medical expert systems

Abstract

Disclosed herein are embodiments of a system that utilizes measurements indicative of respiration in order to estimate an aerobic activity parameter, such as oxygen consumption (VO₂), maximal oxygen consumption (VO₂ max), or energy expenditure (EE). In one embodiment, this system includes at least one inward-facing head-mounted thermal camera (CAM) and a computer. Each CAM, from among the at least one CAM, is configured to take thermal measurements of a region below the nostrils (TH_RBN) of a user. TH_RBN are indicative of an exhale stream of the user, such as an exhale stream from one, or both, of the nostrils and/or the exhale from the mouth. The computer calculates, based on TH_RBN, the aerobic activity parameter. Optionally, the computer generates feature values based on data comprising TH_RBN and utilizes a model to calculate the aerobic activity parameter based on the feature values.

Claims

1. A system configured to estimate an aerobic activity parameter, comprising:

at least one inward-facing head-mounted thermal camera (CAM) configured to take thermal measurements of a region below the nostrils (TH_RBN) of a user;

wherein TH_RBN are indicative of an exhale stream of the user, and

a computer configured to calculate, based on TH_RBN, the aerobic activity parameter that is indicative of one or more of the following;

oxygen consumption (VO₂), maximal oxygen consumption (VO₂ max), and energy expenditure (EE).

2. The system of claim 1, further comprising a frame configured to be worn on the user'"'"'s head;

wherein each CAM, from among the at least one CAM, is physically coupled to the frame, and the frame is further configured to keep the CAM pointed at a certain region below the nostrils of the user when the user'"'"'s head makes angular movements at an angular velocity below 1 rad/sec.

3. The system of claim 2, wherein the frame is further configured to hold each CAM, from among the at least one CAM, such that it does not protrude beyond the tip of the user'"'"'s nose.

4. The system of claim 1, wherein each CAM, from among the at least one CAM, is located above the user'"'"'s upper lip and less than 15 cm from the user'"'"'s face, and does not occlude any of the user'"'"'s mouth and nostrils;

and wherein TH_RN comprise;

(i) thermal measurements of at least one of first and second regions below right and left nostrils (TH_RBN1 and TH_RBN2, respectively) of the user, which are indicative of an exhale stream from the right and left nostrils, respectively, and (ii) thermal measurements of at least one of a region on the mouth and a volume protruding out of the mouth (TH_RBN2) of the user, which are indicative of an exhale stream from the mouth.

5. The system of claim 1, further comprising at least one inward-facing head-mounted visible-light camera configured to take images of a region on the mouth (IM_M) of the user, wherein IM_M are indicative of whether the mouth is open or closed;

and wherein the computer is further configured to utilize IM_M in order to calculate the aerobic activity parameter.

6. The system of claim 1, further comprising at least one inward-facing head-mounted visible-light camera configured to take images of a region on the nose (IM_N) of the user;

wherein the computer is further configured to utilize IM_N to calculate the aerobic activity parameter;

whereby IM_N are indicative of movement of the nose while the user inhales.

7. The system of claim 1, wherein TH_RBN of the user were taken during at least two different periods:

a period during which the user was rested, and a period during which the user conducted physical activity.

8. The system of claim 1, wherein each CAM, from among the at least one CAM, comprises at least one of the following sensors:

a thermopile sensor, and a microbolometer sensor.

9. The system of claim 1, wherein the computer is further configured to generate feature values based on data comprising TH_RBN, and to utilize a model to calculate the aerobic activity parameter based on the feature values;

and wherein the model was trained based on data indicative of aerobic activity of multiple users.

10. The system of claim 9, wherein the computer is further configured to generate, based on TH_RBN, a feature from among the feature values, which is indicative of a respiratory rate of the user.

11. The system of claim 10, wherein the computer is further configured to generate, based on TH_RBN, a feature from among the feature values, which is indicative of a change to the respiratory rate of the user after completing a certain athletic activity.

12. The system of claim 9, wherein the computer is further configured to generate, based on TH_RBN, a feature value from among the feature values, which is indicative of a ratio between an extent to which the user breathed via the mouth and an extent to which the user breathed via the nose.

13. The system of claim 9, wherein the computer is further configured to generate, based on TH_RBN, a feature value from among the feature values, which is indicative of a ratio between durations of exhales of the user and duration of inhales of the user.

14. The system of claim 9, wherein the feature values comprise a feature value indicative of heart rate of the user while TH_RBN were taken.

15. The system of claim 9, wherein the data used to generate the feature values further comprises one or more of the following values:

the age of the user, the gender of the user, the height of the user, and the weight of the user.

16. The system of claim 9, wherein the data used to generate the feature values further comprises values indicative of the activity of the user while TH_RBN of the user were taken.

17. The system of claim 1, wherein the computer is further configured generate, based on TH_RBN, n≥

1 values, x₁, . . . , x_n, of observations of a parameter related to respiration, and to calculate an estimation of a value of the aerobic activity parameter utilizing one or more probability functions of the form P(X=x|θ

), which is a conditional probability of a value of the parameter related to respiration given a value of the aerobic activity parameter is equal to θ

.

18. The system of claim 17, wherein the computer is further configured to perform at least one of the following in order to calculate the estimation value of the aerobic activity parameter:

a maximum likelihood (ML) estimation, and a maximum a posteriori probability (MAP) estimation.

19. The system of claim 1, further comprising an additional inward-facing head-mounted camera configured to take images of exposed facial skin of the user;

wherein the computer is further configured to detect the user'"'"'s heart rate based on the images of exposed facial skin of the user.

20. The system of claim 19, wherein the additional inward-facing head-mounted camera is a video camera.