Portable sEMG Device for Muscle Responsiveness Prediction
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Solution Overview
Problem
Current methods for functional electrical stimulation therapy (FES-T) lack the ability to predict effectively the responsiveness of muscles or muscle groups, leading to inefficient use of therapy time and resources, as the factors determining muscle responsiveness are poorly understood, and early determination of response is crucial for personalized therapy plans.
Innovation Solution
A portable device that records surface electromyography (sEMG) data and uses predetermined relationships to generate a predicted recovery profile for muscles, correlating electrophysiological biomarkers with FES-T responsiveness, enabling personalized therapy plans and efficient resource utilization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If FES-T therapy is administered without prediction of muscle responsiveness, then therapy can be provided to all patients, but therapy time and resources are wasted on muscles that will not respond
Solution Approach 1:
The system performs preliminary assessment of muscle responsiveness to FES-T before initiating the full therapy program. By evaluating muscles in advance and identifying those likely to respond, the system enables clinicians to prioritize therapy time for responsive muscles, thereby avoiding waste of therapy time on non-responsive muscles while maintaining high reliability in prediction through electrophysiological biomarker analysis
2Measurement precision
If comprehensive electrophysiological assessment is performed to predict muscle responsiveness, then prediction accuracy improves, but device complexity and measurement difficulty increase
Solution Approach 1:
The system extracts specific electrophysiological biomarkers from sEMG signals that are most predictive of muscle responsiveness to FES-T. Rather than analyzing all aspects of muscle activity, the system identifies and focuses on key biomarkers such as those related to motor unit recruitment and muscle fiber type composition, thereby achieving high measurement precision while keeping the device and analysis methodology relatively simple
3Adaptability or versatility
If early prediction of muscle responsiveness is implemented, then personalized therapy plans can be created, but requires advanced measurement and analysis capabilities
Solution Approach 1:
The system uses surface electromyography (sEMG) as an intermediary measurement technique to detect electrophysiological biomarkers. sEMG provides a non-invasive window into muscle electrical activity, allowing early prediction of FES-T responsiveness without requiring invasive procedures or overly complex measurement systems. The biomarkers extracted from sEMG data serve as mediators that link early muscle properties to future therapy response, enabling personalized therapy planning
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
The solution allows for early prediction of muscle responsiveness, enabling personalized therapy plans and improving therapy outcomes while optimizing the use of limited treatment time and healthcare resources.
Implementation Method 1
a sensor configured to record surface electromyography (sEMG) data for at least one muscle
Data Source
AI summary
Devices, methods of using devices, and methods of training devices are provided. For example, a portable, hand-held device comprises: a sensor configured to record surface electromyography (sEMG) data for at least one muscle; a memory; and a processor configured to apply predetermined relationships between the sEMG data and reference data stored in the memory, and based on the relationships, generate a predicted recovery profile for the muscle. The device may implement algorithms trained in a functional electrical stimulation therapy (FES-T) program and/or may be used for predicting muscle recovery in the FES-T program.