Tissue Site Detection Using Multi-Wavelength Light Absorption
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Solution Overview
Problem
Existing methods for determining calibration coefficients for accurate oxygenation measurement are inadequate, as they do not effectively account for tissue characteristics or site-specific factors, affecting the accuracy of oxygenation measurement.
Innovation Solution
A device comprising a housing, emitters, detectors, and a processor that determines tissue characteristics or sites using emitted and detected light, allowing for the selection of appropriate calibration coefficients and signal filters, and implementing algorithms to classify tissue sites based on quantitative features of output signals.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional calibration methods are used, then device simplicity is maintained, but measurement precision deteriorates due to inadequate accounting for tissue characteristics
Solution Approach 1:
The system performs preliminary tissue site classification by analyzing light absorption characteristics at multiple wavelengths before conducting oxygenation measurement. This preliminary action identifies tissue type (e.g., finger, earlobe, forehead) and selects appropriate calibration coefficients in advance, ensuring measurement accuracy is optimized for the specific tissue site without requiring complex real-time adjustments during measurement
Solution Approach 2:
The system changes calibration parameters (calibration coefficients) based on detected tissue characteristics. By measuring light absorption at multiple wavelengths and comparing against reference data, the system dynamically selects or adjusts calibration coefficients to match the specific tissue type, thereby improving measurement precision while maintaining a relatively simple device architecture
2Measurement precision
If site-specific calibration is implemented, then measurement precision improves, but device complexity increases due to additional detection and processing requirements
Solution Approach 1:
The tissue site classification process is segmented into distinct stages: initial light emission at multiple wavelengths, absorption measurement, tissue type identification based on absorption patterns, and subsequent selection of appropriate calibration coefficients. This segmentation allows the complex process to be managed through modular, sequential operations rather than a single complex algorithm
Solution Approach 2:
The system incorporates feedback mechanisms where the detected tissue characteristics feed back into the calibration coefficient selection process. The algorithm continuously monitors light absorption patterns and adjusts calibration parameters accordingly, creating a closed-loop system that improves precision while keeping processing requirements manageable through iterative refinement rather than exhaustive computation
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 oxygenation measurement by site-specific calibration, improving the precision of oxygenation assessment and motion detection, thereby enhancing the reliability of physiological parameter monitoring.
Implementation Method 1
The processor is configured to implement an algorithm to determine a tissue characteristic or site based on the emitted light and based on the detected light
Data Source
AI summary
A device includes a housing, an emitter, a detector, and a processor. The housing has a body contact surface. The emitter is coupled to the housing and has an emission surface and has an electrical terminal. The emission surface is configured to emit light proximate the body contact surface in response to a signal applied to the electrical terminal. The detector is coupled to the housing. The detector has a sense surface and an output terminal. The detector is configured to provide an output signal on the output terminal in response to light detected at the sensor surface. The processor is configured to implement an algorithm to determine a tissue site based on the emitted light and based on the detected light.