Systems and Methods for Estimating Surface Electromyography

• US 20090209878A1
• Filed: 04/12/2007
• Published: 08/20/2009
• Est. Priority Date: 04/15/2006
• Status: Active Grant

• Current Assignee: Board of Trustees of the Leland Stanford Junior University (Stanford Management Co.)
• Original Assignee: Board of Trustees of the Leland Stanford Junior University (Stanford Management Co.)
• Inventors: Sanger, Terence D.
• Granted Patent: US 8,311,623 B2
• Time in Patent Office: Days
• US Class Current: 600/546
• CPC Class Codes: A61B 5/389 Electromyography [EMG]
• CPC Class Codes: A61B 5/486 Bio-feedback A61B5/375 takes precedence
• CPC Class Codes: A61F 2/72 Bioelectric control, e.g. myoelectric

Abstract

A rehabilitation device is presented that is inexpensive to produce and is easy to use. The device uses a predictable method to estimate the activity of muscle and therefore provide many ways to train and rehabilitate a person having a prosthetic limb or other neuromuscular disorder.

Claims

1-38. -38. (canceled)

39. A biofeedback device adapted for using the activity of one or more muscles to proportionally control a second electromechanical device, the vibrating biofeedback device comprising a first detector for detecting a first signal, a first signal amplifier, a first signal filter, a computer processor for processing data, storage means for storing data on a storage medium, a first means for initializing the storage medium, a second means for collecting and processing data received from the first signal filter, a power source, and a feedback system, the feedback system comprising a second transmitter, and a second signal generator, and a motorized device, wherein the feedback system is in electrical communication with the computer processor.

40. The biofeedback device of claim 39 wherein the first means comprises a programmable logic circuit configured to prepare the storage means to store collected data.

41. The biofeedback device of claim 39 wherein the second means comprises a programmable logic circuit configured to retrieve information from the storage medium.

42. The biofeedback device of claim 41, wherein the second programmable logic circuit is configured to estimate the likelihood that the first signal comprises a muscle activity signal.

43. The biofeedback device of claim 41, wherein the second programmable logic circuit is configured to use a nonlinear filter to determine the most probable or maximum likelihood value of an unknown muscle control or driving signal.

44. The biofeedback device of claim 39 further comprising a signal transducer.

45. The biofeedback device of claim 39 further comprising output means.

46. The biofeedback device of claim 45 wherein the output means is selected from the group consisting of an audio speaker, a visual display unit, a haptic device, a computer, a computer processor, a computer console, and a radio transmitter.

47. The biofeedback device of claim 46 wherein the signal transducer and the output means are in electrical communication with the computer processor.

48. The biofeedback device of claim 39 wherein the first detector comprises at least one electrode.

49. The biofeedback device of claim 39 wherein the first detector comprises at least two electrodes.

50. The biofeedback device of claim 39 wherein the motorized device selected from the. group consisting of a vibrator, a prosthetic limb device, a device for stimulating muscle, a motorized bicycle, a motor scooter, a motor vehicle, a motorized aircraft, a motorized boat, and a motorized wheelchair.

51. The biofeedback device of claim 39, wherein the first detector is adapted for measuring the first signal and wherein the first signal is a surface electromyography (EMG) signal from a subject.

52. The biofeedback device of claim 39 wherein the first signal filter is a nonlinear filter.

53. The biofeedback device of claim 39 further adapted for use for rehabilitating a subject.

54. The biofeedback device of claim 39 further adapted for use for training a subject.

55. The biofeedback device of claim 39 where the second electromechanical device is selected from the group consisting of an audio speaker, a visual display unit, a haptic device, a computer, a computer processor, a computer console, an infant monitor, a pedometer, and a radio transmitter.

56. The biofeedback device of claim 41 wherein the second means is configured to estimate the maximum likelihood output of the first signal filter and the maximum likelihood output is calculated according the equation MLest=argmax_x p(n|x).

57. The biofeedback device of claim 41 wherein the second means is configured to estimate the maximum a posteriori (MAP) output of the first signal filter and the maximum a posteriori output is calculated using the equation MAP(x(t))=argmax p(x,t)

58. The biofeedback device of claim 41 wherein the second means is further configured to comprise a model, wherein the model is calculated according to a stochastic differential equation selected from the group consisting of dx=f(x)dt+a(x)dw+b(x)dN1, dy=Hydt+CdN2(rx), and p₀(x, t)=(g*p(x, t−

1)+v)/K.

59. The biofeedback device of claim 39 wherein the first signal filter is a bandpass filter.

60. The biofeedback device of claim 39 wherein the second means'"'"' configuration comprises a recursive calculation, wherein the recursive calculation is calculated according to an equation, the equation selected from the group consisting of equations 13, 15, 17, and 18, wherein the recursive calculation estimates a complete conditional probability density at any point in time based on a current and all prior observations of a desired signal.

61. The biofeedback device of claim 53 wherein the subject has a disorder of the nervous system, the disorder selected from the group consisting of cerebral palsy, brain damage resulting from head trauma, brain damage resulting from oxygen deprivation, paraplegic loss of all or part of a limb, spinal cord injury, amyotrophic lateral sclerosis, and stroke.

62. The biofeedback device of claim 53 wherein the subject has a disorder of the muscle, the disorder selected from the group consisting of, AIDS, sarcopenia, disuse atrophy, glucocorticoid-induced muscle loss, and prolonged spaceflight.

63. The biofeedback device of claim 53 wherein the subject has undesired muscle contraction, wherein the undesired muscle contraction is selected from the group consisting of chronic neck pain, back pain, and muscle spasms.

64. The biofeedback device of claim 54 wherein the training is for facilitating muscle activation in a subject, the muscle activation selected from the group consisting muscle activation during sport, dance, and playing a musical instrument.

65. A method for training a subject having a disorder, the method comprising the steps of (i) providing a biofeedback device of claim 39;

(ii) positioning the biofeedback device in contact to the skin surface of the subject having a neuromuscular disorder;

(iii) initiating a movement in the muscle underlying the skin surface of the subject thereby stimulating the muscle of the subject;

(iv) detecting the surface EMG signal;

(v) amplifying the sEMG signal;

(vi) filtering the sEMG signal;

(vii) estimating x(t) using a non-linear filtering algorithm and a predictive model algorithm, the predictive algorithm predicting the output from the muscle and skin;

(viii) converting the estimate of x(t) to a digital signal;

(ix) providing the digital signal to a driver; and

(x) activating a motor in a vibrator;

wherein the activation of the motor stimulates the skin of the subject having a neuromuscular disorder; and

(x) providing feedback to the individual;

wherein the subject learns when the muscle was stimulated, the method resulting in training the subject.

66. The method of claim 65, wherein the disorder is selected from the group consisting of a disorder of the central nervous system, a motor-neuron disorder, a neuromuscular disorder, a muscle disorder, cerebral palsy, brain damage, brain damage resulting from head trauma, brain damage resulting from oxygen deprivation, muscle loss, spinal cord injury, amyotrophic lateral sclerosis, stroke, and paraplegic loss of all or part of a limb.

67. A method for rehabilitating a subject having a disorder, the method comprising the steps of (i) providing a biofeedback device of claim 39;

(ii) positioning the biofeedback device system in contact to the skin surface of the subject having a neuromuscular disorder;

(iii) initiating a movement in the muscle underlying the skin surface of the subject thereby stimulating the muscle of the subject;

(iv) detecting the surface EMG signal;

(v) amplifying the sEMG signal;

(vi) filtering the sEMG signal;

(vii) estimating x(t) using a non-linear filtering algorithm and a predictive model algorithm, the predictive algorithm predicting the output from the muscle and skin;

(viii) converting the estimate of x(t) to a digital signal;

(ix) providing the digital signal to a driver; and

(x) activating a motor in a vibrator;

wherein the activation of the motor stimulates the skin of the subject having a neuromuscular disorder;

(xi) providing feedback to the individual; and

(xii) repeating steps (iii) through (xi);

the method resulting in rehabilitating the subject.

68. The method of claim 67, wherein the disorder is selected from the group consisting of a disorder of the central nervous system, a motor-neuron disorder, a neuromuscular disorder, a muscle disorder, cerebral palsy, brain damage, brain damage resulting from head trauma, brain damage resulting from oxygen deprivation, muscle loss, spinal cord injury, amyotrophic lateral sclerosis, stroke, and paraplegic loss of all or part of a limb.