ASSESSING JOINT CONDITION USING ACOUSTIC SENSORS

• US 20180153501A1
• Filed: 02/02/2018
• Published: 06/07/2018
• Est. Priority Date: 12/08/2015
• Status: Active Grant

• Current Assignee: Kneevoice, Inc.
• Original Assignee: Kneevoice, Inc.
• Inventors: Leal, Carlos, Rigby, Felipe, De Greiff, Gustavo
• Granted Patent: US 10,448,919 B2
• Time in Patent Office: Days
• CPC Class Codes: A61B 5/107 Measuring physical dimensions, e.g. size of the entire body or parts thereof
• CPC Class Codes: A61B 5/4514 Cartilage
• CPC Class Codes: A61B 5/4528 Joints A61B5/4533, A61B5/4538 take precedence
• CPC Class Codes: A61B 7/00 Instruments for auscultation
• CPC Class Codes: A61B 7/006 Detecting skeletal, cartilage or muscle noise A61B7/003 and A61B7/008 take precedence
• CPC Class Codes: A61B 8/0875 for diagnosis of bone A61B5/4504 takes precedence
• CPC Class Codes: G16H 20/30 relating to physical therapies or activities, e.g. physiotherapy, acupressure or exercising
• CPC Class Codes: G16H 50/30 for calculating health indices; for individual health risk assessment

Abstract

A new non-invasive tool for cartilage assessment, exercise and sports management, and prevention of osteoarthritis is provided. In various embodiments, cartilage condition is assessed using audible signals from joints. Assessment test results are used to provide feedback regarding joint stress and friction that is related to physiological or pathological loads. Data obtained from audible signals are processed to provide an index that can be interpreted by a user or third parties. The index is useful as a baseline for exercise practices, training routines, wellness programs, or rehabilitation protocols.

Claims

1. A system comprising:

a contact microphone;

a processor operatively connected to the contact microphone;

a computer readable storage medium having program instructions embodied therewith, the program instructions executable by the processor to cause the processor to perform a method comprising;

receiving from the contract microphone audio comprising sounds emanating from a human patellofemoral joint during knee flexion;

extracting a plurality of features from the audio;

providing the plurality of features to a trained classifier; and

obtaining from the trained classifier a first score indicative of patellofemoral joint health.

2. The system of claim 1, the method further comprising:

capturing the audio via a contact microphone substantially in contact with the exterior of a human knee.

3. The system of claim 2, wherein the contact microphone is substantially in contact with an anterior patellar surface.

4. The system of claim 1, the method further comprising:

canceling noise from the audio prior to extracting the plurality of features.

5. The system of claim 4, wherein canceling noise comprises:

capturing an ambient audio signal;

removing the ambient audio signal from the audio.

6. The system of claim 4, wherein canceling the noise comprises:

applying a bandpass filter to the audio.

7. The system of claim 1, wherein the plurality of features comprises signal frequency, amplitude, zero-crossing rate, entropy of energy, spectral centroid, spectral spread, mel-frequency cepstral coefficients, or chroma vector.

8. The system of claim 1, wherein the trained classifier comprises a random decision forest.

9. The system of claim 1, wherein the trained classifier comprises a neural network.

10. The system of claim 1, wherein the trained classifier comprises a support vector machine.

11. The system of claim 1, the method further comprising:

computing a second score indicative of joint health from the first score.

12. The system of claim 11, wherein computing the second score comprises weighting the first score according to a reported pain value.

13. The system of claim 11, wherein computing the second score comprises weighting the first score according to a characteristic of a subject.

14. The system of claim 13, wherein the characteristic comprises body mass index, age, gender, existing medical condition, or frequency of physical activity.

15. The system of claim 1, the method further comprising outputting the first score or the second score to a user.

16. A method comprising:

receiving audio comprising sounds emanating from a human patellofemoral joint during knee flexion;

extracting a plurality of features from the audio;

providing the plurality of features to a trained classifier; and

obtaining from the trained classifier a first score indicative of patellofemoral joint health.

17. The method of claim 16, further comprising:

capturing the audio via a contact microphone substantially in contact with the exterior of a human knee.

18. The method of claim 17, wherein the contact microphone is substantially in contact with an anterior patellar surface.

19. The method of claim 16, further comprising:

canceling noise from the audio prior to extracting the plurality of features.

20. The method of claim 18, wherein canceling noise comprises:

capturing an ambient audio signal;

removing the ambient audio signal from the audio.

21. The method of claim 18, wherein canceling the noise comprises:

applying a bandpass filter to the audio.

22. The method of claim 16, wherein the plurality of features comprises signal frequency, amplitude, zero-crossing rate, entropy of energy, spectral centroid, spectral spread, mel-frequency cepstral coefficients, or chroma vector.

23. The method of claim 16, wherein the trained classifier comprises a random decision forest.

24. The method of claim 16, wherein the trained classifier comprises a neural network.

25. The method of claim 16, wherein the trained classifier comprises a support vector machine.

26. The method of claim 16, further comprising:

computing a second score indicative of joint health from the first score.

27. The method of claim 26, wherein computing the second score comprises weighting the first score according to a reported pain value.

28. The method of claim 26, wherein computing the second score comprises weighting the first score according to a characteristic of a subject.

29. The method of claim 28, wherein the characteristic comprises body mass index, age, gender, existing medical condition, or frequency of physical activity.

30. The method of claim 16, further comprising outputting the first score or the second score to a user.