Dynamic De-noising Algorithm for Load Cell Signals in Fluid Administration
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Solution Overview
Problem
Existing methods for removing noise from load cell signals used in fluid administration systems are inadequate, as they reduce temporal resolution, making it difficult to accurately determine fluid administration parameters, especially during fluid boluses, and are prone to errors due to movement, such as in ambulances or patient transport vehicles.
Innovation Solution
A de-noising algorithm that dynamically updates line segments fitted to weight data over time, limiting active segments to two and using statistical parameters to identify the best candidate solution, which minimizes noise while maintaining temporal resolution, is implemented to accurately determine fluid administration parameters.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If existing noise removal methods are applied to load cell signals, then noise is reduced, but temporal resolution is reduced making it difficult to accurately determine fluid administration parameters
Solution Approach 1:
The patent applies dynamics by making the filtering approach adaptive rather than static. The system dynamically selects between different filtering methods (no filtering, moving average filtering, or linear regression filtering) based on real-time analysis of signal characteristics and noise levels. This dynamic adaptation allows the system to maintain high temporal resolution when possible while removing noise when necessary, resolving the contradiction between noise removal effectiveness and temporal resolution preservation.
Solution Approach 2:
The patent changes the parameter of filtering intensity based on signal conditions. Instead of applying a fixed filtering method, the system adjusts the filtering parameter (choice of filter type and strength) according to the detected noise level and signal characteristics. This parameter change enables the system to optimize the balance between noise removal and temporal resolution maintenance for different operating conditions.
2Reliability
If existing noise removal methods are applied to load cell signals, then noise is reduced, but accuracy in determining fluid administration parameters deteriorates
Solution Approach 1:
The system dynamically selects the appropriate filtering method based on real-time signal analysis. By continuously monitoring signal characteristics and noise levels, the system adapts its noise removal strategy to preserve the accuracy of fluid administration parameter determination while still removing harmful noise, thus resolving the contradiction between noise removal effectiveness and measurement accuracy.
Solution Approach 2:
The patent implements feedback by analyzing the filtered signal and comparing it with the original signal to assess filtering effectiveness. This feedback mechanism allows the system to adjust filtering parameters and select appropriate filtering methods to maintain accuracy in fluid administration parameter determination while achieving effective noise removal.
3Device complexity
If simple filtering methods are used, then computational complexity is reduced, but noise removal effectiveness deteriorates
Solution Approach 1:
The patent segments the filtering approach into multiple distinct methods (no filtering, moving average filtering, linear regression filtering), each with different complexity levels and noise removal capabilities. The system then selects the appropriate segment (filtering method) based on the specific signal conditions, achieving effective noise removal without unnecessarily increasing computational complexity in all situations.
Solution Approach 2:
The system dynamically selects the appropriate filtering method based on signal characteristics and noise levels. This dynamic selection allows the system to use simpler filtering methods when sufficient for the current conditions, reducing computational complexity, while switching to more sophisticated methods when higher noise removal effectiveness is required.
Data Source
AI summary
A de-noising algorithm is executed dynamically as data is received to generate and update a set of candidate solutions. Each candidate solution is a representation of the data using one or more line segments, and each line segment is fitted to the data within the time period that the segment spans. During each iteration of the algorithm, one candidate solution is identified as a best solution, and properties of the best solution are utilized to dynamically compute properties of the data. To limit the number of active candidate solutions and the corresponding processing power required to update and evaluate them, candidate solutions that fall too far behind the best candidate solution are eliminated from consideration. The de-noising algorithm finds particular utility in the context of a load cell signal that is representative of a weight of an intravenous fluid container.