CN-122028846-A - Method and apparatus for determining muscle activation

Abstract

The present disclosure relates to a computer-implemented method comprising receiving sensor data from one or more myogram sensors of a wearable sensor device, wherein the one or more myogram sensors are configured to monitor muscle activation of a subject, determining whether a first muscle of the subject has been selectively activated based on the sensor data, and providing biofeedback based on the determination of whether the first muscle has been selectively activated.

Inventors

• BENTLEY PAUL
• RAVI VAIDYANATHAN
• Alison McGregor
• PAUL STRATTON
• Renela Zion Bo
• HUO WEIGUANG
• Sachesham Da Wan

Assignees

• 帝国理工学院创新有限公司

Dates

Publication Date

20260512

Application Date

20241009

Priority Date

20231009

Claims (20)

1. 1. A computer-implemented method, comprising: Receiving sensor data from one or more myogram sensors of a wearable sensor device, wherein the one or more myogram sensors are configured to monitor muscle activation of a subject; determining whether a first muscle of the subject has been selectively activated based on the sensor data, and Based on the determination of whether the first muscle has been selectively activated, biofeedback is provided.
2. 2. The method of claim 1, wherein determining whether the first muscle of the subject has been selectively activated comprises: determining a calibration activation value for said first muscle during a calibration procedure, and Determining the degree of activation of the first muscle relative to the calibrated activation value.
3. 3. The method of claim 2, wherein determining whether a first muscle of the subject has been selectively activated comprises determining whether a degree of activation of the first muscle exceeds a threshold percentage of the calibrated activation value.
4. 4. A method according to any one of claims 1 to 3, wherein the one or more myogram sensors comprise one or more Mechanical Myogram (MMG) sensors.
5. 5. The method of any one of claims 1 to 4, wherein determining whether the first muscle has been selectively activated based on the sensor data includes classifying the sensor data using one or more trained classifiers.
6. 6. The method of claim 5, wherein the one or more trained classifiers are configured to distinguish between sensor data indicative of muscle activation and sensor data indicative of muscle rest.
7. 7. The method of claim 6, wherein the one or more trained classifiers are trained using Electromyography (EMG) sensor data indicative of muscle activation and EMG sensor data indicative of muscle rest.
8. 8. The method of any of claims 5-7, wherein the one or more trained classifiers are configured to identify sensor data indicative of a degree of activation of the first muscle relative to a second muscle of the subject.
9. 9. The method of claim 8, wherein the one or more trained classifiers are trained using EMG sensor data indicative of the degree of activation of the first muscle relative to the second muscle of the subject.
10. 10. The method of any of claims 5 to 9, wherein the one or more trained classifiers are configured to identify an activation strength of the first muscle.
11. 11. The method of claim 10, wherein the one or more trained classifiers are trained using EMG sensor data indicative of different activation strengths of the first muscle.
12. 12. The method of any one of claims 1 to 11, wherein determining whether a first muscle of the subject has been selectively activated comprises determining a degree of activation of the first muscle relative to a second muscle of the subject.
13. 13. The method of claim 12, wherein a first one of the one or more myogram sensors is configured to monitor muscle activation of the first muscle and a second one of the one or more myogram sensors is configured to monitor muscle activation of the second muscle.
14. 14. The method of any one of claims 1 to 13, wherein the first muscle is a core muscle.
15. 15. The method of any one of claims 1 to 14, wherein: The first muscle is a transverse abdominal muscle and a first muscle pattern sensor of the one or more muscle pattern sensors is configured to monitor muscle activation of the transverse abdominal muscle, or The first muscle is an intra-abdominal oblique muscle, and a first muscle map sensor of the one or more muscle map sensors is configured to monitor muscle activation of the intra-abdominal oblique muscle.
16. 16. The method of claim 15 when dependent on claim 12, wherein the second muscle is rectus abdominus and a second of the one or more myogram sensors is configured to monitor muscle activation of the rectus abdominus.
17. 17. The method of any one of claims 1 to 14, wherein the first muscle is a erector spinal muscle, and a first muscle map sensor of the one or more muscle map sensors is configured to monitor muscle activation of the erector spinal muscle.
18. 18. The method of any one of claims 1 to 17, further comprising: receiving motion sensor data from one or more motion sensors configured to monitor motion of the subject, and Processing the motion sensor data to determine a posture of the subject.
19. 19. The method of any one of claims 1 to 18, wherein: the sensor data is received from the wearable sensor device including the one or more myogram sensors, and Providing biofeedback includes providing a command for the wearable sensor device to provide one or more of tactile feedback, audio feedback, and visual feedback to the subject.
20. 20. A computer-readable medium comprising instructions that, when executed by at least one processor of an apparatus, cause the apparatus to perform the method of any one of claims 1 to 19.

Description

Method and apparatus for determining muscle activation Technical Field The present disclosure relates to a method and apparatus for determining muscle activation. Background Lower Back Pain (LBP) is the leading cause of chronic disability and pain. Secondary consequences of lower back pain include depression, analgesic abuse, employment sickness or loss of business. The evidence-based guidelines recommend back exercises as first line therapy. These may be implemented by self-leading items (e.g., using a leaflet or video), or by personalized supervision by looking at the therapist. However, compliance with self-administered exercise programs is often poor, while waiting time for a therapist to see may be long and contact time with the therapist may be short. The intensity of back exercises has been shown to be proportional to the degree of back pain relief. Thus, methods to improve back pain self-management may prove to be very cost-effective compared to, for example, increasing the number of therapists. Two major obstacles to back pain self-management are that people find it difficult to exercise properly (e.g., activate deep abdominal muscles, rather than shallow abdominal muscles), and the aggressiveness of such exercises is low, especially in cases of depression and apathy in the population. Back exercises involving specific activation of pelvic stabilization muscles have a better therapeutic effect on lower back pain than non-specific exercises. However, guiding patients to selectively activate the trunk muscles is challenging and has heretofore required physical therapists to palpation manually, or expensive, laboratory-based instruments such as ultrasound or standard EMG (i.e., involving wires connected to an amplifier that is not worn on the body). Accordingly, there is a need for a convenient, practical method and apparatus for improving self-management of back pain, particularly for encouraging and supervising users to perform back exercises properly (e.g., at home). This includes helping the user actively activate specific core muscle patterns associated with relief of back pain and disability. Disclosure of Invention This summary introduces concepts that are described in more detail in the detailed description. It is not intended to identify essential features of the claimed subject matter, nor is it intended to be used to limit the scope of the claimed subject matter. According to a first aspect of the present disclosure, there is provided a computer-implemented method comprising receiving sensor data from one or more myogram sensors of a wearable sensor device, wherein the one or more myogram sensors are configured to monitor muscle activation of a subject, determining whether a first muscle of the subject has been selectively activated based on the sensor data, and providing biofeedback based on the determination of whether the first muscle has been selectively activated. By determining whether a muscle has been selectively activated, it may be identified whether an exercise being performed by the subject is activating an intended muscle group (i.e., including the first muscle). By providing feedback to the subject regarding the selective activation of the first muscle, the subject can adjust the manner in which their exercises are performed to increase the activation of the desired muscle group, thereby enabling them to more effectively perform a particular exercise (e.g., a back exercise for alleviating LBP). The behavioral effects of MMG-based feedback on muscle selective activation have shown that providing such feedback results in a subject improving the selectivity of muscle activation. Determining whether a first muscle of the subject has been selectively activated may include determining a calibration activation value for the first muscle during a calibration procedure, and determining a degree of activation of the first muscle relative to the calibration activation value. Determining whether a first muscle of the subject has been selectively activated may include determining whether a degree of activation of the first muscle exceeds a threshold percentage of the calibrated activation value. The one or more myogram sensors may include one or more Mechanical Myogram (MMG) sensors. Unlike Electromyography (EMG), MMG does not require electrical connection to the skin, avoids the need to use adhesives, gels, or shaves, and is not sensitive to perspiration. In addition, fewer sensors are required (EMG requires positive, negative and ground electrodes) and no signal amplification is required. This allows the microphone-MMG to be used in low cost personal devices that can measure muscle activity "anytime and anywhere". In contrast, existing EMG sensors are wired and not wearable, and thus are not suitable for use in wearable sensor devices. Thus, the one or more myogram sensors may include one or more wireless myogram sensors. As an alternative to using MMG sensors, the one or more myogram se