Electrode-to-Muscle Mapping Calibration for Autonomous FES
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Solution Overview
Problem
Existing FES systems face challenges in efficiently determining electrode energization patterns for producing desired movements due to individual anatomical variations and the need for frequent recalibration, which limits user autonomy and requires labor-intensive manual adjustment by a physical therapist.
Innovation Solution
An iterative calibration method using metaheuristic optimization, such as Differential Evolution, to determine optimized electrode energization patterns, incorporating sensor feedback, a priori information, and user input to minimize discomfort, and optimizing multiple movements simultaneously.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual calibration by physical therapist is used to determine electrode energization patterns, then individual anatomical variations can be accommodated, but the process becomes labor-intensive and requires frequent recalibration
Solution Approach 1:
The system performs self-calibration by automatically determining electrode energization patterns through iterative optimization algorithms that use sensor feedback to refine the mapping between electrodes and muscle activation, eliminating the need for manual calibration by physical therapists
Solution Approach 2:
The system incorporates sensor feedback mechanisms that monitor the actual movement produced by electrode stimulation and use this information to iteratively adjust and optimize the electrode energization patterns, ensuring accurate mapping without manual intervention
2Measurement precision
If manual calibration is performed to account for individual variations and device positioning, then accurate movement production is achieved, but user autonomy is reduced
Solution Approach 1:
The system enables users to independently calibrate their own devices through automated algorithms that adapt to individual anatomical variations and device positioning, restoring user autonomy while maintaining movement production accuracy
Solution Approach 2:
The system automatically adjusts electrode energization parameters based on real-time sensor feedback and iterative optimization, adapting to individual user characteristics without requiring manual calibration, thereby enabling user autonomy while maintaining precision
3Measurement precision
If frequent recalibration is performed to maintain accuracy, then electrode-muscle mapping precision is improved, but time consumption increases
Solution Approach 1:
The system performs rapid automated self-calibration that determines accurate electrode-muscle mappings in minimal time through efficient optimization algorithms, eliminating the time-consuming manual recalibration process while maintaining high precision
Solution Approach 2:
The system performs initial calibration during device setup or first use, establishing an accurate electrode-muscle mapping that can be maintained over time, reducing the frequency of subsequent recalibration events while preserving precision
4Measurement precision
If multiple movements are calibrated separately to ensure accuracy, then each movement's electrode pattern is optimized, but the calibration process becomes more complex
Solution Approach 1:
The system combines multiple movement calibrations into a unified optimization process that simultaneously determines electrode energization patterns for multiple movements, reducing overall complexity while maintaining individual movement optimization through integrated sensor feedback and iterative refinement
Data Source
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AI summary
A functional electrical stimulation (FES) device includes electrodes arranged to apply functional electrical stimulation to a body part of the user. FES stimulation is performed by: receiving values of a set of user metrics for the user; receiving a target position of the body part represented as values for a set of body part position measurements; determining a user-specific energization pattern for producing the target position based on the received target position and the received values of the set of user metrics for the user; and energizing the electrodes of the FES device in accordance with the determined user-specific energization pattern. The determination may utilize an FES calibration database with records having fields containing: values of the set of user metrics for reference users; energization patterns; and values of the set of body part position metrics for positions assumed by the body part in response to applying the energization patterns.