Real-time EMG Feedback for Muscle Activation Ratios
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Solution Overview
Problem
Current methods fail to effectively change muscle activity in real-time during complex movements, such as walking, especially in individuals with musculoskeletal issues, as they often require complex interpretations of continuous EMG signals and kinematic changes, leading to slow motor learning and limited retention of new coordination strategies.
Innovation Solution
A training method using real-time electromyography (EMG) feedback that processes EMG signals from multiple muscles to provide actionable data points during a stance phase, allowing subjects to adjust muscle activation ratios without altering kinematics, using visual, auditory, or tactile cues to facilitate rapid learning and retention of new muscle coordination strategies.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If continuous EMG signals are provided for interpretation, then muscle activity information is available, but the complexity of interpretation increases and motor learning slows down
Solution Approach 1:
The patent segments continuous EMG signals into discrete, actionable data points representing specific muscle activation ratios during stance and swing phases. This segmentation transforms complex continuous signals into manageable discrete units that are easier to interpret and act upon in real-time.
Solution Approach 2:
The patent changes the parameter representation from continuous EMG amplitudes to discrete activation ratios between muscle pairs. This parameter transformation simplifies the information structure, making it more suitable for rapid human interpretation and motor learning during dynamic movements.
2Adaptability or versatility
If complex kinematic changes are required to alter muscle activity, then muscle coordination can be modified, but the complexity of the training protocol increases and learning retention decreases
Solution Approach 1:
The patent implements real-time feedback of muscle activation ratios during stance and swing phases, allowing subjects to directly observe and adjust their muscle coordination patterns. This feedback mechanism enables muscle coordination modification without requiring complex kinematic instructions, simplifying the training protocol while maintaining adaptability.
3Productivity
If real-time EMG feedback is provided during complex movements, then muscle activation can be modified rapidly, but the system complexity and processing requirements increase
Solution Approach 1:
The patent extracts only the essential information needed for motor learning - the activation ratio between specific muscle pairs during specific gait phases. By taking out only the critical data points rather than processing and presenting all EMG information, the system achieves rapid motor learning with reduced processing complexity.
Data Source
AI summary
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.