Identification of the dominant nostril using thermal measurements

• US 10,064,559 B2
• Filed: 12/06/2017
• Issued: 09/04/2018
• 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:
  US15/832,879
• Publication Number:
  US 20180092547A1
• Time in Patent Office: 272 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 5/0816 Measuring devices for examining respiratory frequency measuring frequency of electric signals G01R23/00
• CPC Class Codes: A61B 5/0935 Recording apparatus specially adapted therefor
• 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 5/168 Evaluating attention deficit, hyperactivity
• 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: A61B 5/7267 involving training the classification device
• CPC Class Codes: A61B 7/00 Instruments for auscultation
• CPC Class Codes: A61B 7/003 Detecting lung or respiration noise
• CPC Class Codes: A61B 7/04 Electric stethoscopes microphones, acoustic transducers therefor H04R
• CPC Class Codes: G01J 2005/0077 Imaging
• CPC Class Codes: G01J 5/0014 for sensing the radiation from gases, flames
• CPC Class Codes: G01J 5/0025 Living bodies ear thermometers G01J5/0011; detecting, measuring or recording for diagnostic purposes A61B5/00
• CPC Class Codes: G01J 5/10 using electric radiation detectors
• CPC Class Codes: G02B 2027/0181 Adaptation to the pilot/driver
• CPC Class Codes: G02B 27/017 : Head mounted
• CPC Class Codes: G06F 18/214 : Generating training patterns; Bootstrap methods, e.g. bagging or boosting
• CPC Class Codes: G06F 18/24 : Classification techniques
• CPC Class Codes: G06F 2203/011 : Emotion or mood input determined on the basis of sensed human body parameters such as pulse, heart rate or beat, temperature of skin, facial expressions, iris, voice pitch, brain activity patterns
• CPC Class Codes: G06F 2218/12 : Classification; Matching
• CPC Class Codes: G06F 3/012 : Head tracking input arrangements
• CPC Class Codes: G06F 3/0304 : Detection arrangements using opto-electronic means constructional details of pointing devices not related to the detection arrangement using opto-electronic means G06F3/033; optical digitisers G06F3/042
• CPC Class Codes: G06N 20/00 : Machine learning
• CPC Class Codes: G06N 3/006 : based on simulated virtual individual or collective life forms, e.g. social simulations or particle swarm optimisation [PSO]
• CPC Class Codes: G06N 3/044 : Recurrent networks, e.g. Hopfield networks
• CPC Class Codes: G06N 3/045 : Combinations of networks
• CPC Class Codes: G06N 3/08 : Learning methods
• CPC Class Codes: G06N 5/01 : Dynamic search techniques; Heuristics; Dynamic trees; Branch-and-bound
• CPC Class Codes: G06N 7/01 : Probabilistic graphical models, e.g. probabilistic networks
• CPC Class Codes: G06T 2207/30201 : Face
• CPC Class Codes: G06T 7/0014 : using an image reference approach
• CPC Class Codes: G06T 7/60 : Analysis of geometric attributes
• CPC Class Codes: G06V 40/10 : Human or animal bodies, e.g. vehicle occupants or pedestrians; Body parts, e.g. hands
• CPC Class Codes: G06V 40/166 : using acquisition arrangements
• CPC Class Codes: G06V 40/171 : Local features and components; Facial parts eye characteristics G06V40/18; Occluding parts, e.g. glasses; Geometrical relationships
• CPC Class Codes: G09B 19/00 : Teaching not covered by other main groups of this subclass teaching or practice apparatus for gun-aiming or gun-laying F41G3/26
• CPC Class Codes: G09B 23/28 : for medicine
• 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

Brain activity, and in particular which hemisphere is relatively more effective, is correlated with the dominant nostril (i.e., the nostril through which most of the air is exhaled when breathing through the nose). Thus, identifying which of the nostrils is dominant may have various applications. Described herein are systems and methods for identifying the dominant nostril. In one embodiment, a system includes at least one inward-facing head-mounted thermal camera (CAM) and a computer. The at least one CAM does not occlude any of the user'"'"'s mouth and nostrils and is used to take thermal measurements of first and second regions below the right and left nostrils (TH_ROI1 and TH_ROI2, respectively). The computer identifies the dominant nostril based on TH_ROI1 and TH_ROI2. Optionally, the computer detects, utilizing TH_ROI1 and TH_ROI2, the three-dimensional (3D) shape of the exhale stream from at least one of the nostrils.

Claims

1. A system configured to identify the dominant nostril, comprising:

at least one inward-facing head-mounted thermal camera (CAM) configured to take thermal measurements of first and second regions below the right and left nostrils (TH_ROI1 and TH_ROI2, respectively);

wherein the at least one CAM does not occlude any of the user'"'"'s mouth and nostrils; and

a computer configured to identify the dominant nostril based on TH_ROI1 and TH_ROI2.

2. The system of claim 1, wherein each CAM from among the at least one CAM weighs below 10 g, comprises multiple sensing elements, and is physically coupled to a frame configured to be worn on the user'"'"'s head;

wherein the frame is configured to hold each CAM less than 15 cm from the user'"'"'s face and above the user'"'"'s upper lip.

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

wherein the at least one CAM comprises first and second inward-facing head-mounted thermal cameras (CAM1 and CAM2, respectively) configured to take TH_ROI1 and TH_ROI2, respectively;

wherein the frame is configured to hold CAM1 and CAM2 less than 15 cm from the user'"'"'s face; and

wherein CAM1 is physically coupled to the right half of the frame and captures the exhale stream from the right nostril better than it captures the exhale stream from the left nostril, and CAM2 is physically coupled to the left half of the frame and captures the exhale stream from the left nostril better than it captures the exhale stream from the right nostril.

4. The system of claim 1, wherein the computer is further configured to learn typical sequence of switching between the dominant nostrils based on previous measurements of the user taken over more than a week, and to issue an alert upon detecting an irregularity in the sequence of switching between the dominant nostrils.

5. The system of claim 1, wherein the computer is further configured to identify balanced breathing when breaths through the right and the left nostrils are equal, and to notify the user accordingly.

6. The system of claim 1, wherein the computer is further configured to monitor nostril dominance over a certain period, and issue an alert when at least one of the following occurs:

(i) a ratio between the total times of the right and left nostril dominance during the certain period reaches a threshold, (ii) an average time to switch from right to left nostril dominance reaches a threshold, and (iii) an average time to switch from left to right nostril dominance reaches a threshold.

7. The system of claim 1, wherein the computer is further configured to detect that the user is experiencing an asthma attack or has a headache, and to command a user interface to update the user about the dominant nostril and to suggest the user to switch the dominant nostril.

8. The system of claim 1, wherein the computer is further configured to provide, via a user interface, biofeedback that helps the user to achieve a required breathing pattern.

9. The system of claim 1, wherein TH_ROI1 and TH_ROI2 are indicative of length of the exhale stream, and the computer is further configured to calculate a level of excitement of the user based on the length of the exhale stream.

10. The system of claim 1, wherein the computer is further configured to identify a shape of the exhale stream (SHAPE) based on TH_ROI1 and TH_ROI2, and to differentiate between at least first and second shapes of the exhale stream (SHAPEs).

11. The system of claim 10, wherein the first SHAPE is identified based on a first set of TH_ROI1 and TH_ROI2 taken during multiple exhales over a first duration longer than a minute, the second SHAPE is identified based on a second set of TH_ROI1 and TH_ROI2 taken during multiple exhales over a second duration longer than a minute, and the first duration precedes the second duration.

12. The system of claim 10, wherein the computer is further configured to learn a sequence of typical changes between different SHAPEs based on previous measurements of the user taken over more than a week, and to issue an alert upon detecting an irregularity in a sequence of changes between the different SHAPEs.

13. The system of claim 10, wherein the computer is further configured to suggest the user to eat a first type of food responsive to identifying the first SHAPE, and suggest the user to eat a second type of food responsive to identifying the second SHAPE.

14. The system of claim 13, wherein the computer is further configured to receive data about types of foods consumed by the user, store the data in a memory, and find correlations between the SHAPEs and the types of foods.

15. The system of claim 10, wherein the computer is further configured to prioritize activities for the user based on the identified SHAPE, such that a first activity is prioritized over a second activity responsive to identifying the first SHAPE, and the second activity is prioritized over the first activity responsive to identifying the second SHAPE.

16. The system of claim 10, wherein the computer is further configured to receive an indication of the user'"'"'s breathing rate, and to:

(i) suggest to the user, via a user interface, to perform a first activity in response to detecting that the breathing rate reached a threshold while identifying the first SHAPE, and (ii) suggest to the user to perform a second activity, which is different from the first activity, in response to detecting that the breathing rate reached the threshold while identifying the second SHAPE.

17. The system of claim 10, wherein the computer is further configured to receive an indication of the user'"'"'s breathing rate, and to:

(i) alert the user, via a user interface, in response to detecting that the breathing rate reached a threshold while identifying the first SHAPE, and (ii) not alert the user in response to detecting that the breathing rate reached the threshold while identifying the second SHAPE.

18. The system of claim 10, wherein the computer is configured to identify the SHAPE by comparing TH_ROI1 and TH_ROI2 to one or more reference patterns that were generated based on previous TH_ROI1 and TH_ROI2 of the user taken on different days.

19. The system of claim 10, wherein identifying the SHAPE comprises:

generating feature values based on TH_ROI1 and TH_ROI2, and utilizing a model to classify TH_ROI1 and TH_ROI2 to a class corresponding to the first shape or the second shape, based on the feature values; and

wherein the model is trained based on previous TH_ROI1 and TH_ROI2 of the user taken during different days.

20. The system of claim 10, wherein the first and second SHAPEs are indicative of at least one of the following:

two of the five great elements according to Vedas, two different emotional states of the user, two different moods of the user, two different energetic levels of the user, and a healthy state of the user versus an illness state of the user.