FEM and EIT for NMES Garment Recalibration
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Solution Overview
Problem
Current neuromuscular electrical stimulation (NMES) devices, such as the NeuroLife® sleeve, rely on a trial and error process for optimizing electrode patterns and stimulation parameters, which is time-consuming and inefficient due to inter-session and inter-subject variability in electrode positioning and anatomical differences, making fine adjustments difficult and requiring manual recalibration.
Innovation Solution
The development of a finite element model (FEM) for current flow in the anatomical human forearm to determine optimal stimulation parameters and the use of electrical impedance tomography (EIT) for autonomous recalibration, allowing for automatic adjustment of electrode arrays to ensure consistent alignment and optimal muscle activation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a trial and error process is used to optimize electrode patterns, then electrode patterns can be adjusted to achieve desired movement, but the process becomes time-consuming and labor-intensive
Solution Approach 1:
The system performs preliminary actions by automatically determining optimal electrode patterns and stimulation parameters before actual use. The FEM model pre-calculates current flow paths and muscle activation predictions, while the EIT system pre-maps anatomical variations, eliminating the need for time-consuming trial-and-error calibration during clinical application.
Solution Approach 2:
The system enables self-service through autonomous recalibration capabilities. After garment donning, the EIT system automatically detects electrode positioning and anatomical variations, the FEM model independently calculates optimal stimulation parameters, and the system self-adjusts without requiring manual operator intervention or iterative trial-and-error processes.
2Measurement precision
If manual calibration through trial and error is performed, then electrode patterns can be optimized for individual subjects, but the process is tedious and inefficient
Solution Approach 1:
The patent replaces manual mechanical trial-and-error adjustment with automated computational systems. The FEM model uses numerical simulations to predict current flow and muscle activation, while the EIT system uses electrical impedance measurements to map anatomy, substituting manual calibration operations with automated computational and measurement processes that achieve higher precision without sacrificing productivity.
Solution Approach 2:
The FEM model and EIT system serve as intermediaries between the electrode garment and the target muscles. These intermediary systems automatically translate electrode positioning information into optimized stimulation parameters by calculating current flow paths and predicting muscle activation, eliminating the need for direct manual trial-and-error adjustment while maintaining high measurement precision.
3Ease of operation
If discrete electrode states are used for calibration, then electrodes can be individually activated, but fine adjustments become difficult due to coarse resolution
Solution Approach 1:
The system transitions from static discrete electrode states to dynamic continuous parameter adjustment. The FEM model enables real-time calculation of optimized current amplitudes and distribution patterns based on actual electrode positioning and anatomical variations, allowing fine-grained continuous adjustment of stimulation parameters rather than relying on fixed discrete electrode activation states.
Solution Approach 2:
The patent applies parameter changes by continuously adjusting stimulation parameters such as current amplitude, pulse width, and electrode activation patterns based on FEM model predictions and EIT measurements. This allows fine precision control of muscle activation by varying parameters continuously rather than being constrained to discrete electrode states, while maintaining ease of operation through automated parameter optimization.
4Adaptability or versatility
If garment position is shifted during donning, then the garment can be fitted to different subjects, but system calibration is affected and realignment is required
Solution Approach 1:
The EIT system provides feedback by automatically detecting actual electrode positioning and anatomical variations after garment donning. This feedback information is fed into the FEM model, which then calculates corrected stimulation parameters to compensate for position shifts, maintaining calibration consistency without requiring manual realignment procedures while preserving garment fitting flexibility across different subjects.
Solution Approach 2:
The system implements dynamic adaptation to garment position variations through automated recalibration. The EIT system dynamically maps actual electrode positions, the FEM model dynamically recalculates current flow paths based on measured anatomical variations, and the stimulation parameters are dynamically adjusted to maintain optimal muscle activation, ensuring reliability despite changes in garment positioning during donning.
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
This approach enables efficient determination of muscle groups activated by different stimulation parameters and provides a method for autonomous recalibration, reducing the need for manual adjustments and improving the precision and efficiency of NMES/EMG treatments.
Implementation Method 1
performing an EIT measurement across an electrode array of an electrode garment and constructing an anatomical model based on the EIT measurement
Implementation Method 2
generating a finite element model (FEM) of current flow in an anatomical human forearm
Data Source
AI summary
Systems and methods for generating a finite element model (FEM) of current flow in an anatomical human forearm are disclosed. The disclosed FEM may assist in determining optimal stimulation parameters in electrical stimulation systems for achieving movement of paralyzed limbs or enhancement of able limbs. This model will allow users to determine which muscle groups are receiving stimulation under different parameters. Systems and methods which leverage electrical impedance tomography (EIT) for autonomous recalibration following garment donning are also disclosed. The method may comprise performing an EIT measurement across an electrode array of an electrode garment and constructing an anatomical model based on the EIT measurement. Next, one or more alignment variations may be estimated based on an alignment variation model. Finally, the electrode array is adjusted, automatically or manually, to accommodate the alignment variations using an alignment adjustment function.