Shear Wave Tissue Load Sensor with Machine Learning Calibration
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Solution Overview
Problem
Current methods for measuring tissue loads, such as stress in ligaments and tendons, are invasive, cumbersome, and require extensive calibration, limiting their application outside laboratory settings.
Innovation Solution
A device that uses shear wave propagation through tissue, combined with machine learning, to provide absolute load measurements without individual calibration, by training on a teaching set of shear wave signals from multiple individuals, allowing for clinically significant tissue loading information to be obtained directly.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If calibration to individual is performed to obtain absolute stress measurements, then measurement precision is improved, but device complexity and time consumption increase
Solution Approach 1:
The system performs self-calibration by automatically extracting calibration factors from the shear wave signals themselves. The machine learning model processes the shear wave data and independently determines the calibration factors needed for absolute stress calculation, eliminating the need for separate manual calibration procedures and external calibration references.
Solution Approach 2:
A machine learning model serves as an intermediary between the shear wave measurements and the absolute stress calculations. The model processes the shear wave signals, extracts calibration information, and converts the measurements into absolute stress values, simplifying the overall measurement system while maintaining precision.
2Measurement precision
If calibration process is implemented to convert shear wave speed to absolute load values, then measurement precision is improved, but loss of time increases
Solution Approach 1:
The system performs preliminary extraction of calibration factors directly from the shear wave signals during the measurement process itself. By pre-processing the signal to extract calibration information before the final stress calculation, the system eliminates the need for separate time-consuming calibration steps while maintaining measurement accuracy.
3Measurement precision
If invasive measurement techniques are used to measure tissue load, then measurement precision is improved, but object-affected harmful factors increase
Solution Approach 1:
The patent replaces invasive mechanical measurement techniques with a non-invasive acoustic shear wave measurement system. Instead of inserting transducers directly into tissue or using force plates requiring tissue section measurement, the system uses surface-mounted sensors to detect shear wave propagation through the tissue, eliminating invasive procedures while maintaining measurement capability.
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
Enables accurate, clinically significant tissue loading information to be obtained without time-consuming calibration, providing robust and calibration-equivalent loading data in motion studies, simplifying measurements and facilitating cross-individual comparisons.
Implementation Method 1
a stimulator/monitor having a stimulator probe adapted to apply a transverse stimulation to tissue of an individual at a first location along a longitudinal axis to produce a shear wave traveling through the tissue along the longitudinal axis
Implementation Method 2
having at least one motion sensor detecting transverse motion of the tissue at a predetermined second location along the longitudinal axis separated from the first location to provide a measured shear wave signal
Data Source
AI summary
Measurement of an induced shear wave in tensioned tissue of a given individual is provided to a machine learning system trained to determine absolute load from shear wave signal data. The machine learning system uses a teaching set linking shear wave signal data to absolute load, however, does not require normal calibration data based on measured loads allowing reduced or no calibration for absolute load determinations.