Surface electromyography index

• US 7,593,769 B1
• Filed: 04/28/2007
• Issued: 09/22/2009
• Est. Priority Date: 02/14/2006
• Status: Active Grant

• Current Assignee: IQ Biolabs Incorporated
• Original Assignee: IQ Biolabs Incorporated
• Inventors: Ettare, Ryan Dennis
• Primary Examiner(s): Hindenburg; Max
• Assistant Examiner(s): SZMAL, BRIAN SCOTT
• Application Number:
  US11/741,742
• Time in Patent Office: 878 Days
• Field of Search: 600/300, 600/409, 600544-546, 600/587, 600/591, 600/595, 600/554, 128/898, 128/905, 340/825.19, 341/21, 345/157, 463/36, 607/45
• US Class Current: 600/547
• CPC Class Codes: A61B 5/0002 Remote monitoring of patients using telemetry, e.g. transmission of vital signals via a communication network A61B5/07 takes precedence; transmission systems for measured values G08C; transmission H04B; transmission of digital information H04L; wireless communication networks H04W
• CPC Class Codes: A61B 5/389 Electromyography [EMG]

Abstract

Methods and apparatus for surface electromyography (sEMG) index calculation and applications. The sEMG data points are quantified by accessing sEMG data points describing activity of a muscle. A single value is calculating from the sEMG data points. The single value is calculated using the sEMG data points as an input to a mathematical algorithm. The mathematical algorithm can include an integral. The mathematical algorithm can also include consideration of high points, low points, averages within the sEMG data points, first-order derivatives, second-order derivatives, and other direct or indirect data based at least in part upon sEMG data. The E-index calculated can be used in many applications and in combination with EKG and EEG.

Claims

1. A method for quantifying surface electromyography (sEMG) data acquired from an individual using one or more sensors, the method comprising:

a processor accessing sEMG data points describing muscular activity of the individual, the sEMG data points being acquired from the individual using the one or more sensors;

the processor assigning weights to at least two different muscle groups; and

the processor calculating at least one E-index value summarizing the sEMG data points comprising;

calculating the index value based at least in part on an area under a curve, the curve approximating at least a portion of the sEMG data points;

calculating the index value based at least in part on an area over the curve;

or

calculating the index value based at least in part on a combination of a high data point, a low data point, an average of multiple data points, and/or a derivative of one or more order.

2. A method according to claim 1, wherein calculating the at least one index value comprises:

calculating the index value based at least in part on an average of multiple high data points or an average of multiple low data points.

3. A method according to claim 1, wherein the area under the curve or the area over the curve is calculated by taking an integral of at least a portion of an sEMG graph representing the sEMG data points.

4. A method according to claim 1, wherein at least two index values are calculated at different time intervals.

5. A method according to claim 4, wherein the time interval for at least two index values corresponds to a different task performed by the individual.

6. A method according to claim 1, wherein an sEMG data point or an E-index value of a muscle group or groups in the individual'"'"'s forearm and wrist is emphasized to estimate a likelihood of repetitive strain injury.

7. A method for assessing a medical condition comprising:

performing the method of claim 1; and

the processor comparing E-index with a result of electrocardiography and/or electroencephalography to assess the medical condition.

8. A method for evaluating health for a population of individuals, comprising performing the method of claim 1 for each individual in the population.

9. A method for comparing the health of multiple populations comprising:

performing the method of claim 8 for individuals in a first population;

performing the method of claim 8 for individuals in a second population; and

the processor comparing the E-indexes for the individuals of the first population to the E-indexes for the individuals of the second population.

10. A method for assessing a likelihood of developing a medical condition, comprising:

performing the method of claim 1; and

the processor comparing the calculated E-index value for the individual to E-index values of other individuals to assess the relative likelihood of developing the medical condition.

11. A method according to claim 10, wherein the medical condition is a repetitive strain injury, repetitive motion injury, cumulative trauma disorder, or work-related upper extremity disorder.

12. A method for evaluating a risk associated with a product, comprising:

performing the method of claim 1 for several individuals before, during, and/or after the individual uses the product to calculate an E-index for each individual; and

the processor assessing the risk associated with the product based on the E-index calculated for each individual.

13. A method according to claim 12, wherein the risk associated with the product is a health hazard due to an ergonomic factor.

14. A method for detecting a false response, comprising:

performing the method of claim 1, wherein the E-index calculated relates to an involuntary muscle response of the individual; and

the processor detecting whether the response was false based at least in part on the E-index calculated.

15. A method for detecting muscle response to control an animated character, the method comprising:

performing the method of claim 1; and

the processor associating the E-index with an action of the animated character.

16. A method according to claim 1, further comprising acquiring the sEMG data from the individual.

17. A computer readable medium having computer executable instructions stored on the computer readable medium, the executable instructions being configured to cause a computer to perform the acts of claim 1 when executed by the computer.

51. A method for evaluating a change in muscular health of the individual due to treatment, the method comprising:

performing the method of claim 1 prior to the treatment to calculate a pre-treatment E-index value;

performing the method of claim 1 after the treatment to calculate a post-treatment E-index value; and

the processor comparing the pre-treatment E-index value to the post-treatment E-index value to evaluate a change in muscular health and/or state due to the treatment.

52. A method according to claim 51, wherein at least one pre-treatment index value and post treatment index value are calculated for at least two different muscle groups.

18. A method for quantifying surface electromyography (sEMG) data acquired from an individual using one or more sensors, the method comprising:

a processor accessing sEMG data points describing muscular activity of the individual, the sEMG data points being acquired from the individual using the one or more sensors;

the processor calculating at least one E-index value summarizing the sEMG data points, comprising;

calculating the index value based at least in part on an area under a curve, the curve approximating at least a portion of the sEMG data points, or

calculating the index value based at least in part on an area over the curve, wherein the area under the curve or the area over the curve is calculated by taking a weighted integral or double integral of at least a portion of an sEMG graph representing the sEMG data points.

19. A method according to claim 18, wherein at least two index values are calculated at different time intervals.

20. A method according to claim 19, wherein the time interval for at least two index values corresponds to a different task performed by the individual.

21. A method for assessing a medical condition comprising:

performing the method of claim 18; and

the processor comparing the E-index with a result of electrocardiography and/or electroencephalography to assess the medical condition.

22. A method for evaluating a change in muscular health of the individual due to treatment, the method comprising:

performing the method of claim 18 prior to the treatment to calculate a pre-treatment E-index value;

performing the method of claim 18 after the treatment to calculate a post-treatment E-index value; and

the processor comparing the pre-treatment E-index value to the post-treatment E-index value to evaluate a change in muscular health and/or state due to the treatment.

23. A method according to claim 22, wherein at least one pre-treatment index value and post treatment index value are calculated for at least two different muscle groups.

24. A method for evaluating health for a population of individuals, comprising performing the method of claim 18 for each individual in the population.

25. A method for comparing the health of multiple populations comprising:

performing the method of claim 24 for individuals in a first population;

performing the method of claim 24 for individuals in a second population; and

the processor comparing the E-indexes for the individuals of the first population to the E-indexes for the individuals of the second population.

26. A method for assessing a likelihood of developing a medical condition, comprising:

performing the method of claim 18; and

the processor comparing the calculated E-index value for the individual to E-index values of other individuals to assess the relative likelihood of developing the medical condition.

27. A method according to claim 26, wherein the medical condition is a repetitive strain injury, repetitive motion injury, cumulative trauma disorder, or work-related upper extremity disorder.

28. A method for evaluating a risk associated with a product, comprising:

performing the method of claim 18 for several individuals before, during, and/or after the individual uses the product to calculate an E-index for each individual; and

the processor assessing the risk associated with the product based on the E-index calculated for each individual.

29. A method according to claim 28, wherein the risk associated with the product is a health hazard due to an ergonomic factor.

30. A method for detecting a false response, comprising:

performing the method of claim 18, wherein the E-index calculated relates to an involuntary muscle response of the individual; and

the processor detecting whether the response was false based at least in part on the E-index calculated.

31. A method for detecting muscle response to control an animated character, the method comprising:

performing the method of claim 18; and

the processor associating the E-index with an action of the animated character.

32. A method according to claim 18, further comprising acquiring the sEMG data from the individual.

33. A computer readable medium having computer executable instructions stored on the computer readable medium, the executable instructions being configured to cause a computer to perform the acts of claim 18 when executed by the computer.

34. A method for quantifying surface electromyography (sEMG) data acquired from an individual using one or more sensors, the method comprising:

a processor accessing sEMG data points describing muscular activity of the individual, the sEMG data points being acquired from the individual using the one or more sensors;

the processor calculating at least one E-index value summarizing the sEMG data points, wherein calculating the at least one index value comprises;

calculating the index value based at least in part on a combination of a high data point (H), a low data point (L), and an average data point (A) according to the following equation;

DH+CL+BA=E

where D, C, and B describe weights assigned to the high data point, low data point, and average data point.

35. A method according to claim 34, wherein D is about equal to about 0.3, C is about equal to about 0.6, and B is about equal to about 0.1.

36. A method according to claim 34, wherein at least two index values are calculated at different time intervals.

37. A method according to claim 36, wherein the time interval for at least two index values corresponds to a different task performed by the individual.

38. A method for assessing a medical condition comprising:

performing the method of claim 34; and

the processor comparing the E-index with a result of electrocardiography and/or electroencephalography to assess the medical condition.

39. A method for evaluating a change in muscular health of the individual due to treatment, the method comprising:

performing the method of claim 34 prior to the treatment to calculate a pre-treatment E-index value;

performing the method of claim 34 after the treatment to calculate a post-treatment E-index value; and

the processor comparing the pre-treatment E-index value to the post-treatment E-index value to evaluate a change in muscular health and/or state due to the treatment.

40. A method according to claim 39, wherein at least one pre-treatment index value and post treatment index value are calculated for at least two different muscle groups.

41. A method for evaluating health for a population of individuals, comprising performing the method of claim 34 for each individual in the population.

42. A method for comparing the health of multiple populations comprising:

performing the method of claim 41 for individuals in a first population;

performing the method of claim 41 for individuals in a second population; and

the processor comparing the E-indexes for the individuals of the first population to the E-indexes for the individuals of the second population.

43. A method for assessing a likelihood of developing a medical condition, comprising:

performing the method of claim 34; and

the processor comparing the calculated E-index value for the individual to E-index values of other individuals to assess the relative likelihood of developing the medical condition.

44. A method according to claim 43, wherein the medical condition is a repetitive strain injury, repetitive motion injury, cumulative trauma disorder, or work-related upper extremity disorder.

45. A method for evaluating a risk associated with a product, comprising:

performing the method of claim 34 for several individuals before, during, and/or after the individual uses the product to calculate an E-index for each individual; and

the processor assessing the risk associated with the product based on the E-index calculated for each individual.

46. A method according to claim 45, wherein the risk associated with the product is a health hazard due to an ergonomic factor.

47. A method for detecting a false response, comprising:

performing the method of claim 34, wherein the E-index calculated relates to an involuntary muscle response of the individual; and

the processor detecting whether the response was false based at least in part on the E-index calculated.

48. A method for detecting muscle response to control an animated character, the method comprising:

performing the method of claim 34; and

the processor associating the E-index with an action of the animated character.

49. A method according to claim 34, further comprising acquiring the sEMG data from the individual.

50. A computer readable medium having computer executable instructions stored on the computer readable medium, the executable instructions being configured to cause a computer to perform the acts of claim 34 when executed by the computer.