Implantable Sensor Recalibration Using Piecewise Linear Regression
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Solution Overview
Problem
Existing recalibration techniques for implantable sensors are inadequate for sensors with non-linear outputs, as they often fail to maintain accuracy over time due to drift characteristics and environmental noise, especially when the output changes in a non-linear fashion.
Innovation Solution
A method involving data compilation, adjustment of sensor parameters, and curve adjustment using historical and empirical data, including piecewise linear regression, to establish a new sensor output, ensuring accurate calibration across the sensor's lifespan.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If simple linear adjustment is used during recalibration, then the recalibration process is simple and quick, but the sensing system accuracy is limited and only maintained over a small range
Solution Approach 1:
The calibration curve is divided into multiple linear segments (piecewise linear approximation) rather than using a single linear adjustment. This segmentation allows the system to handle non-linear sensor behavior by applying different linear adjustments to different ranges of sensor output, thereby improving accuracy across the full measurement range while keeping each individual adjustment relatively simple.
Solution Approach 2:
The recalibration method transitions from static linear adjustment to dynamic piecewise linear adjustment. The system adapts the calibration approach based on the sensor's non-linear characteristics by selecting appropriate linear segments for different operating ranges, allowing the calibration to dynamically respond to the sensor's actual behavior throughout its lifespan.
2Loss of time
If simple linear adjustment is used, then the recalibration is quick to perform, but the accuracy cannot be maintained for extended periods
Solution Approach 1:
The system performs preliminary characterization of the sensor's non-linear behavior during initial calibration and uses this information to pre-establish piecewise linear calibration segments. This preliminary action allows subsequent recalibrations to be quicker while maintaining accuracy, as the system already has the framework for handling non-linearities built into its calibration model.
Solution Approach 2:
The method changes the calibration parameters from simple linear offset adjustments to piecewise linear parameters that capture non-linear sensor drift characteristics. By incorporating multiple parameters representing different linear segments, the system can accurately model and compensate for non-linear drift over extended periods while keeping the recalibration process manageable.
3Measurement precision
If piecewise linear adjustment with five pieces is used, then the sensor accuracy is improved across the full range, but the computational complexity increases
Solution Approach 1:
The system uses a moderate number of pieces (five) in the piecewise linear approximation - enough to capture the essential non-linear behavior of the sensor without over-complicating the computational model. This partial action approach provides sufficient accuracy improvement while avoiding the excessive computational burden that would result from using a much larger number of segments.
4Adaptability or versatility
If the sensor output changes in a non-linear fashion, then the sensor can capture complex environmental variations, but simple linear recalibration fails to account for the true nature of the sensing element
Solution Approach 1:
The recalibration method transitions from static linear adjustment to dynamic piecewise linear adjustment that adapts to the sensor's non-linear behavior. The system selects and applies appropriate linear segments based on the sensor's current operating point, allowing the calibration to dynamically respond to the sensor's actual non-linear characteristics and maintain accuracy across varying environmental conditions.
Data Source
AI summary
An implantable sensing system that includes a sensor for sensing a biological parameter, a processor connected to the sensor for processing the parameter and a drug delivery unit connected to the processor for responding to the processor based on the parameter. The processor is programmed to adjust an output of the sensor by compiling an array of data relating to the sensor, adjusting a sensor parameter a first time based on data in the array, adjusting a curve representing the sensor output based on data in the array and adjusting the sensor parameter a second time based on data in the array.