Selecting a stressor based on thermal measurements of the face

• US 10,045,726 B2
• Filed: 12/06/2017
• Issued: 08/14/2018
• 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
• Primary Examiner(s): Porta, David
• Assistant Examiner(s): BOOSALIS, FANI POLYZOS
• Application Number:
  US15/832,844
• Publication Number:
  US 20180092587A1
• Time in Patent Office: 251 Days
• CPC Class Codes: A61B 2562/0271 Thermal or temperature sensors
• CPC Class Codes: A61B 5/0075 by spectroscopy, i.e. measuring spectra, e.g. Raman spectroscopy, infrared absorption spectroscopy A61B5/0071 takes precedence
• 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 5/163 by tracking eye movement, gaze, or pupil change
• CPC Class Codes: A61B 5/165 Evaluating the state of mind, e.g. depression, anxiety
• CPC Class Codes: A61B 5/4884 inducing physiological or psychological stress, e.g. applications for stress testing
• CPC Class Codes: A61B 5/6803 Head-worn items, e.g. helmets, masks, headphones or goggles
• CPC Class Codes: A61B 5/6814 Head
• CPC Class Codes: G01J 5/0025 Living bodies ear thermometers G01J5/0011; detecting, measuring or recording for diagnostic purposes A61B5/00
• 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 2027/0141 characterised by the informative content of the display
• CPC Class Codes: G02B 2027/0178 Eyeglass type eyeglass details G02C
• CPC Class Codes: G06F 16/337 Profile generation, learning or modification
• CPC Class Codes: G06F 16/436 using biological or physiological data of a human being, e.g. blood pressure, facial expression, gestures
• CPC Class Codes: H04N 23/611 where the recognised objects include parts of the human body
• CPC Class Codes: H04N 5/33 Transforming infrared radiation cameras or camera modules for generating image signals from infrared radiation H04N23/20; circuitry of SSIS for transforming infrared radiation into image signals H04N25/20

Abstract

Described herein are systems and methods for selecting a stressor based on thermal measurements. In one embodiment, a system includes an inward-facing head-mounted thermal camera (CAM) and a computer. CAM takes thermal measurements of a region on a periorbital area (TH_ROI1) of the user. The computer detects extents of stress based on TH_ROI1, receives indications of potential stressors to which the user was exposed while TH_ROI1 were taken, and selects the stressor, from among the potential stressors, based on the indications and the extents. Optionally, during most of the time the user was affected by the stressor, the effect of the stressor, as manifested via changes to TH_ROI1, was higher than the effects of most of the potential stressors. Optionally, thermal measurements of other regions on the face may also be utilized to detect the extents of stress.

Claims

1. A system configured to select a stressor, comprising:

an inward-facing head-mounted thermal camera (CAM) configured to take thermal measurements of a region on a periorbital area (TH_ROI1) of a user; and

a computer configured to;

detect extents of stress based on TH_ROI1;

receive indications of potential stressors to which the user was exposed while TH_ROI1 were taken; and

select the stressor, from among the potential stressors, based on the indications and the extents.

2. The system of claim 1, wherein the region on the periorbital area is a region of the periorbital area of the right eye of the user;

and further comprising a second inward-facing head-mounted thermal camera (CAM2) configured to take thermal measurements of a region on the periorbital area of the left eye (TH_ROI2) of the user;

wherein the computer is further configured to detect the stress level also based on TH_ROI2; and

further comprising a frame configured to be worn on the user'"'"'s head, and to hold each of CAM and CAM2 less than 10 cm from the user'"'"'s head;

wherein each of CAM and CAM2 is weighs below 10 g.

3. The system of claim 1, further comprising second and third inward-facing head-mounted thermal cameras configured to take thermal measurements of regions on the forehead (TH_ROI2) of the user and the nose (TH_ROI3) of the user, respectively;

and wherein the computer is further configured to detect the extents of stress also based on TH_ROI2 and TH_ROI3.

4. The system of claim 1, further comprising a second inward-facing head-mounted thermal camera configured to take thermal measurements of a region below the nostrils (TH_ROI2) of the user;

wherein TH_ROI2 are indicative of the user'"'"'s breathing, and the computer is further configured to detect the extents of stress also based on TH_ROI2.

5. The system of claim 1, further comprising a camera configured to take images of the surroundings of the user;

wherein the computer is further configured to generate at least some of the indications based on analysis of the images.

6. The system of claim 1, wherein the computer selects the stressor based on at least one of the following assumptions (i) an average stress level of the user while being affected by the stressor is greater than an average stress level of the user while not being affected by the stressor, and (ii) during most of the time the user was affected by the stressor, an effect of the stressor, as manifested via changes to TH_ROI1, was higher than effects of most of the potential stressors.

7. The system of claim 6, wherein the indications comprise a certain indication of a stressor that is indicative of at least one of the following:

the user conducted a certain activity, the user was at a certain location, and the user interacted with a certain entity.

8. The system of claim 6, wherein the computer is further configured to calculate, based on the indications and the extents, an effect of each potential stressor, and to select as the stressor a potential stressor with a maximal effect.

9. The system of claim 1, wherein the computer is further configured to generate a machine learning-based model based on the indications and extents, and to select the stressor based on an analysis of the model.

10. The system of claim 9, wherein the model comprises parameters of at least one of the following models:

a regression model, a nave Bayes model, a Bayes network, a support vector machine for regression, and a neural network model.

11. The system of claim 9, wherein the computer is further configured to perform the analysis of the model by evaluating parameters of the model that correspond to the potential stressors, and to select as the stressor a certain potential stressor that has a corresponding parameter that is indicative of an effect that reaches a threshold, while effects indicated in parameters corresponding to most of the potential stressors do not reach the threshold.

12. The system of claim 9, wherein the computer is further configured to perform the analysis of the model by providing a predictor module with various inputs that correspond to different potential stressors that affect the user, and to calculate, based on the inputs and the model, various predicted stress levels;

and wherein the stressor is selected from among the potential stressors, responsive to identifying that;

(i) based on a first subset of the various predicted stress levels, the effect of the stressor reaches a threshold, and (ii) based on a second subset of the various predicted stress levels, the effects of most of the potential stressors do not reach the threshold.

13. A method for selecting a stressor, comprising:

taking, utilizing an inward-facing head-mounted thermal camera (CAM), thermal measurements of a region on a periorbital area (TH_ROI1) of a user;

detecting extents of stress based on TH_ROI1;

receiving indications of times during which the user was exposed to potential stressors; and

selecting the stressor, from among the potential stressors, based on the indications and the extents.

14. The method of claim 13, wherein the region on the periorbital area is a region of the periorbital area of the right eye of the user;

and further comprising;

taking, utilizing a second inward-facing head-mounted thermal camera, thermal measurements of a region on the periorbital area of the left eye TH_ROI2) of the user, and selecting the stressor also based on TH_ROI2.

15. The method of claim 13, further comprising taking, utilizing a second inward-facing head-mounted thermal camera, thermal measurements of a region on the forehead (TH_ROI2), and selecting the stressor also based on TH_ROI2.

16. The method of claim 13, further comprising taking, utilizing a second inward-facing head-mounted thermal camera, thermal measurements of a region on the nose (TH_ROI2), and selecting the stressor also based on TH_ROI2.

17. The method of claim 13, further comprising taking, utilizing a second inward-facing head-mounted thermal camera, thermal measurements of a region below the nostrils (TH_ROI2) of the user, and selecting the stressor also based on TH_ROI2;

wherein TH_ROI2 are indicative of the user'"'"'s breathing.

18. The method of claim 13, further comprising generating a machine learning-based model based on the indications and extents, and selecting the stressor based on an analysis of the model.

19. The method of claim 18, further comprising performing the analysis of the model by evaluating parameters of the model that correspond to the potential stressors, and selecting as the stressor a certain potential stressor that has a corresponding parameter that is indicative of an effect that reaches a threshold, while effects indicated in parameters corresponding to most of the other potential stressors do not reach the threshold.

20. The method of claim 18, further comprising performing the analysis of the model by:

(i) providing a predictor module with various inputs that correspond to different potential stressors that affect the user;

(ii) calculating, based on the inputs and the model, various predicted stress levels;

(iii) determining, based on the various predicted stress levels, effects of the potential stressors; and

(iv) selecting the stressor based on the effects;

wherein an effect of the stressor reaches a threshold, while effects of most of the other potential stressors do not reach the threshold.