Granted Patent B2

US 12,303,256 · App. 17/413,918 · Granted May 20, 2025

Inventors: Michael G. Fanton (Stanford, CA); David B. Camarillo (Aptos, CA); Kaveh Laksari (Stanford, CA); Lyndia Chun Wu (Stanford, CA); Mehmet Kurt (Stanford, CA); Taylor H. Nguyen (Stanford, CA)

Assignee: The Board of Trustees of the Leland Stanford Junior University

A61B5/1122A61B5/0002A61B5/6803A61B5/7267G16H50/50A61B2503/10A61B2562/0219

Abstract

Claims (37)

1. A system for assessment of head kinematics and real-time detection of concussive events, comprising:

a head-mounted device configured to being worn on or secured to a head of an individual, wherein the head-mounted device comprises one or more accelerometers for measuring angular head motion when worn or secured to the head of the individual; and

a computer system in communication with the head-mounted device via an interface, wherein the computer system comprises a memory and a processor that reads instructions stored in the memory, wherein the instructions direct the processor to:

capture in real time, utilizing the one or more accelerometers, brain angle measurements in one or more anatomical directions;

compute in real time, utilizing the captured brain angle measurements as input in a mathematical mass-spring-damper model, a brain angle metric for the one or more anatomical directions; and

determine in real time, utilizing the brain angle metric as input within a trained classifier, that a concussive event occurs, wherein the trained classifier is trained to classify whether the computed brain angle metric is indicative of a concussive event or a non-concussive event.

2. The system of claim 1 , wherein the brain angle metric is a maximum brain angle.

3. The system of claim 1 , wherein the brain angle metric is a vector of three peak brain angle values for three anatomical directions.

4. The system of claim 1 , wherein the mathematical mass-spring-damper model models a rotational deformation of a brain from skull loading.

5. The system of claim 1 , wherein the mathematical mass-spring-damper model computes motion for each anatomical direction utilizing:

I ({umlaut over (θ)} brain +{umlaut over (θ)} skull )=− kθ brain −c{dot over (θ)} brain

wherein I is a moment of inertia of a mass, k and c are a stiffness and damping values of the system, respectively, and θ brain and θ skull represent angles of a brain and a skull, respectively.

6. The system of claim 1 , wherein the classifier is a regression model.

7. The system of claim 6 , wherein the regression model is:

p injury =(1 +e −β 0 −Σβ i x i ) −1

where p injury is a probability of concussive event, x i are components of an injury criterion, and β i are fitted coefficients, with i=1, . . . , n, representing each of the at least one anatomical directions and n representing a number of anatomical directions.

8. The system of claim 1 , wherein the concussive event is determined by a risk curve that determines a percent likelihood that a head impact was the concussive event.

9. The system of claim 1 , wherein the one or more accelerometers comprises a rotational accelerometer or an array of linear accelerometers.

10. The system of claim 1 , wherein the head-mounted device is: a helmet, a mouthguard, a hat, an ear protection, an eye-wear, a skin-mounted sensor, or a head band.

11. A real-time method for assessment of head kinematics and detection of concussive events, comprising:

fitting a head-mounted device on a head of an individual, wherein the head-mounted device comprises one or more accelerometers for measuring angular head motion;

capturing in real time, utilizing the one or more accelerometers, brain angle measurements in one or more anatomical directions;

computing in real time, utilizing the captured brain angle measurements as input in a mathematical mass-spring-damper model and a computer system, a brain angle metric for the one or more anatomical directions; and

determining in real time, utilizing the brain angle metric as input within a trained classifier and the computer system, that a concussive event occurs, wherein the trained classifier is trained to classify whether the brain angle metric is a concussive event or a non-concussive event.

12. The method of claim 11 , wherein the brain angle metric is a maximum brain angle.

13. The method of claim 11 , wherein the brain angle metric is a vector of three peak brain angle values for three anatomical directions.

14. The method of claim 11 , wherein the mathematical mass-spring-damper model models a rotational deformation of a brain from skull loading.

15. The method of claim 11 , wherein the mathematical mass-spring-damper model computes motion for each anatomical direction utilizing:

I ({umlaut over (θ)} brain +{umlaut over (θ)} skull )=− kθ brain −c{dot over (θ)} brain

wherein I is a moment of inertia of a mass, k and c are a stiffness and damping values of the system, respectively, and θ brain and θ skull represent angles of a brain and a skull, respectively.

16. The method of claim 11 , wherein the classifier is a regression model.

17. The method of claim 16 , wherein the regression model is:

p injury =(1 +e −β 0 −Σβ i x i ) −1

where p injury is a probability of the concussive event, x i are components of an injury criterion, and β i are fitted coefficients, with i=1, . . . , n, representing each of the at least one anatomical directions and n representing a number of anatomical directions.

18. The method of claim 11 , wherein the concussive event is determined by a risk curve that determines a percent likelihood that a head impact was the concussive event.

19. The method of claim 11 , wherein the one or more accelerometers comprises a rotational accelerometer or an array of linear accelerometers.

20. The method of claim 11 , wherein the head-mounted device is: a helmet, a mouthguard, a hat, an ear protection, an eye-wear, a skin-mounted sensor, or a head band.

Continuity (2)

References Cited (73)