Real-time electromyography feedback to change muscle activity during complex movements

Abstract

A training method is provided to train changing muscle contribution in a human subject while the human subject is performing a complex movement. Feedback is provided in a simple understandable fashion by one or more data points calculated based on electromyography signals obtained over e.g. a stance phase of a walking cycle. The training method showed a significantly increased training effect in subjects performing these complex movements where these subjects were able to change the muscle activation given a specific goal. The training could be setup of a single muscle or multiple muscles. The data point feedback could be a measure for the single muscle or some relative measure for the multiple muscles. The training method results in improved coordination strategies and could be useful for retraining purposes as well as intervention methods for musculoskeletal pathologies or movement disorders.

Claims

1. A training method of changing relative muscle contribution in a human subject while the human subject is performing a complex movement, comprising: (a) a human subject performing a complex movement, wherein the complex movement distinguishes a stance phase where a leg is in contact with a surface followed by a swing phase where the leg is swinging through air and is not in contact with the surface, and wherein the leg cycles through a sequence of stance phases and swing phases during the complex movement; (b) recording electromyography from a first muscle over a first stance phase, and processing the recorded electromyography from the first muscle; (c) recording electromyography from a second muscle over the first stance phase, and processing the recorded electromyography from the second muscle; (d) calculating a data point for the first stance phase, wherein the data point represents a relative muscle contribution of the first muscle and the second muscle over the first stance phase, wherein the data point is calculated using the recorded and processed electromyography of both the first muscle and the second muscle over the first stance phase, and wherein the calculating step occurs during the first swing phase immediately after the completion of the first stance phase and prior to the start of a subsequent stance phase immediately following the first swing phase; and (e) providing feedback of the data point to the human subject performing the complex movement during the first swing phase and prior to the start of the subsequent stance phase immediately following the first swing phase, wherein the feedback is intended for the human subject to change the relative muscle contribution of the first muscle and the second muscle in the subsequent stance phase, wherein the feedback of the data point is in the form of a visual, auditory, or tactile cue, and wherein the feedback is informative to the human subject whether to increase or decrease the first muscle activation relative to the second muscle activation, or vice versa, during the subsequent stance phase.

2. The method as set forth in claim 1, wherein further comprising calculating another data point for the first stance phase, and providing feedback of the other data point to the human subject performing the complex movement during the first swing phase and prior to the start of the subsequent stance phase immediately following the first swing phase.

3. The method as set forth in claim 1, wherein the human subject is given a goal to change the data point by changing the first muscle activation relative to the second muscle activation, or vice versa.

4. The method as set forth in claim 1, wherein the human subject is not given a goal to change kinematics of the complex movement.

5. The method as set forth in claim 1, wherein the human subject is given a goal to maintain kinematics of the complex movement.

6. The method as set forth in claim 1, wherein the processing of the recorded electromyography from the first and second muscle comprises applying a bandpass filter, rectifying, lowpass filtering to create a linear envelope for the first muscle and a linear envelope of the second muscle.

7. The method as set forth in claim 1, wherein the processing of the recorded electromyography from the first and second muscle comprises normalizing to an electromyography standard.

8. The method as set forth in claim 1, wherein the recording, processing and feedback is accomplished by a computer processor capable of receiving the electromyography from the first and second muscle, capable of processing the received electromyography from the first and second muscle, capable of calculating the data point based on the processed electromyography from the first and second muscle, and capable of providing the feedback to the human subject.

9. A training method of changing a muscle contribution in a human subject while the human subject is performing a complex movement, comprising: (a) a human subject performing a complex movement, wherein the complex movement distinguishes a stance phase where a leg is in contact with a surface followed by a swing phase where the leg is swinging through air and is not in contact with the surface, and wherein the leg cycles through a sequence of stance phases and swing phases during the complex movement; (b) recording electromyography from a muscle over a first stance phase, and processing the recorded electromyography from the muscle; (c) calculating a data point for the first stance phase, wherein the data point represents a muscle contribution of the muscle over the first stance phase, wherein the data point is calculated using the recorded and processed electromyography of the muscle over the first stance phase, and wherein the calculating step occurs during the first swing phase immediately after the completion of the first stance phase and prior to the start of a subsequent stance phase immediately following the first swing phase; and (d) providing feedback of the data point to the human subject performing the complex movement during the first swing phase and prior to the start of the subsequent stance phase immediately following the first swing phase, wherein the feedback is intended for the human subject to change the muscle contribution of the muscle in the subsequent stance phase, wherein the feedback of the data point is in the form of a visual, auditory, or tactile cue, and wherein the feedback is informative to the human subject whether to increase or decrease the muscle activation relative to a target during the subsequent stance phase.

10. The method as set forth in claim 9, wherein further comprising calculating another data point for the first stance phase, and providing feedback of the other data point to the human subject performing the complex movement during the first swing phase and prior to the start of the subsequent stance phase immediately following the first swing phase.

11. The method as set forth in claim 9, wherein the human subject is given a goal to change the data point by changing the muscle activation.

12. The method as set forth in claim 9, wherein the human subject is not given a goal to change kinematics of the complex movement.

13. The method as set forth in claim 9, wherein the human subject is given a goal to maintain kinematics of the complex movement.

14. The method as set forth in claim 9, wherein the processing of the recorded electromyography comprises applying a bandpass filter, rectifying, lowpass filtering to create a linear envelope for the muscle.

15. The method as set forth in claim 9, wherein the processing of the recorded electromyography comprises normalizing to an electromyography standard.

16. The method as set forth in claim 9, wherein the recording, processing and feedback is accomplished by a computer processor capable of receiving the electromyography, capable of processing the received electromyography, capable of calculating the data point based on the processed electromyography, and capable of providing the feedback to the human subject.

Description

BRIEF DESCRIPTION OF THE DRAWINGS

[0020] FIG. 1 shows a training method according to an exemplary embodiment of the invention.

[0021] FIG. 2 shows EMG processing according to an exemplary embodiment of the invention.

[0022] FIGS. 3A-B show according to an exemplary embodiment of the invention representative real-time feedback from the second and third feedback (FB) trials (FIG. 3A). Percent changes in activation ratio and stance-phase-averaged muscle activity from baseline (FIG. 3B). (p<0.05: # different than 0, * different than first feedback trial)

DETAILED DESCRIPTION

[0023] The example provided herein pertain to walking which is based on a study to understand the feasibility whether humans have the ability of conscious control over individual muscles in real-time during complex movements such as walking to be able to make these changes that could improve their mobility. As a skilled artisan would readily understand, this example can be extended to other complex movements.

Methods

[0024] Medial gastrocnemius and soleus electromyograms were recorded from 5 healthy adults walking on a treadmill. The gastrocnemius-to-soleus activation ratio was computed after each step by dividing the gastrocnemius by soleus stance-phase-averaged electromyogram linear envelope. Subjects performed a 1-minute baseline trial followed by three 6-minute feedback trials. In the first feedback trial, subjects were asked to reduce their activation ratio which was displayed as a real-time bar plot. During the second and third feedback trials, the bars were colored according to gastrocnemius activation (FIG. 3A), encouraging subjects to reduce their activation ratio by decreasing gastrocnemius activity. For the first four minutes of each feedback trial, subjects were instructed to explore different strategies to change their activation without changing their kinematics. They were then asked to walk consistently with their best strategy during the final 2 minutes of each trial. Following the 3 feedback trials, subjects performed a 6-minute retention trial without feedback. The final 30 steps of each trial were analyzed and compared with t-tests (a=0.05).

Results

[0025] Compared to baseline, the gastrocnemius-to-soleus activation ratio decreased during all trials by 24-27% (p<0.05). During the first feedback trial, when only activation ratio was displayed, subjects reduced their activation ratio (p=0.005) compared to baseline by increasing soleus activity (p=0.004). During the third feedback trial, when activation ratio and gastrocnemius activation were displayed, subjects decreased gastrocnemius (p=0.033) activity compared to the first feedback trial, but showed no further reduction in activation ratio (p=0.882, FIG. 3B).

[0026] Based on these results, human subjects were able to voluntarily modulate redundant muscle activation during walking with real-time visual feedback. Improved coordination strategies emerged when subjects were shown gastrocnemius activation and activation ratio, compared to activation ratio alone. Coordination retraining, as we have performed here, could become a promising intervention for musculoskeletal pathologies.

[0027] Applications of embodiments of the invention could be relative muscle activation feedback for movement disorders. Clinical benefits of the present invention are: [0028] Osteoarthritis: The field has sought to reduce joint loading with bracing and by drastically changing the way that people move their limbs while walking. Patients are often non-compliant with these interventions due to the stigma of walking awkwardly or wearing a cumbersome brace. After learning a modified muscle coordination strategy from our device, individuals can walk without any visible changes, but be achieving a similar joint load reduction. Additionally, our device could be coupled with the standard of care to achieve additional reduction in joint loading. [0029] Patellofemoral Pain and Stroke: The standard of care is clinic-based therapy which works on strengthening, active range of motion, and performing functional movements. These protocols are often ineffective at altering the underlying muscle coordination deficiencies. By directly measuring and giving feedback on the relative activity of involved muscles, patients may be able to alter their coordination strategies, thus improving mobility and symptoms.