Infusion Drip Chamber Imaging for Precise Gravity Flow Control
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Solution Overview
Problem
Existing gravity-fed infusion systems suffer from inaccuracies in flow rate measurement due to variable drop sizes and non-linear compression characteristics, and they lack robustness against ambient light and fluid transparency, while conventional pumps add cost and complexity.
Innovation Solution
An image-based system with a transparent drip chamber, infrared backlight, and image sensors analyzes drop formation and meniscus trends using spline interpolation and physics-based estimation, combined with confidence-weighted data fusion and adaptive illumination to achieve precise flow regulation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional optical interruption sensors are used to detect drop passage, then the system structure is simple, but measurement precision deteriorates due to inability to estimate flow during drop formation and sensitivity to ambient light and fluid transparency
Solution Approach 1:
The patent replaces mechanical/optical interruption sensors with an image-based vision system that captures images of the drip chamber and uses computer vision algorithms to detect and measure drop formation, detachment, and passage. This substitution enables continuous flow estimation during drop formation while reducing sensitivity to ambient light conditions through digital image processing techniques.
Solution Approach 2:
The system changes the measurement parameters by transitioning from discrete drop passage detection to continuous drop formation monitoring. By capturing images at multiple time points and analyzing drop volume changes, the system estimates instantaneous flow rate during drop formation, providing more precise and continuous flow measurement.
2Ease of operation
If manual roller or slide clamps are used for flow regulation, then device complexity is low, but measurement precision and control accuracy deteriorate due to non-linear compression characteristics
Solution Approach 1:
The patent implements a feedback control system where the image-based flow measurement continuously monitors actual flow rate, and this information is used to adjust the clamp position to achieve the desired target flow rate. The system calculates required clamp adjustments based on the difference between measured and target flow rates, enabling precise flow control despite the non-linear characteristics of manual clamps.
3Measurement precision
If structured illumination patterns are used to image the drip chamber, then measurement precision improves through enhanced sensitivity, but device complexity increases due to alignment requirements and manufacturing complexity
Solution Approach 1:
The patent extracts and removes the complex structured illumination components (patterned backplate, alignment mechanisms) from the system. Instead, it uses a simple uniform light source combined with computer vision algorithms to achieve accurate drop detection and measurement, thereby reducing manufacturing complexity and alignment requirements while maintaining measurement precision.
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
The system provides real-time, accurate flow rate measurement and regulation without mechanical pumps, maintaining compliance with medical safety standards and reducing complexity and cost.
Implementation Method 1
infrared backlight
Implementation Method 2
image sensors
Implementation Method 3
gravity-driven flow
Data Source
AI summary
This disclosure relates to a gravity-driven infusion system that uses image-based monitoring to regulate flow. A contrasting, infrared-backlit wall and camera capture pendant drops and meniscus levels within a transparent drip chamber. The controller defines a baseline referenced to the spout or meniscus and fits sparse spline points on the drop perimeter to derive geometric functionals, such as neck width and centroid height. Temporal changes of these functionals map directly to instantaneous flow without explicit volume integration. An optional Young-Laplace model provides a physics-based boundary and confidence metric. A meniscus trend yields a low-frequency flow estimate. Confidence-weighted fusion controls a flow-control valve that compresses a multi-lumen insert. An independent safety occluder and watchdog ensure fail-safe shutdown. A medication library stores fluid-aware calibration. Multi-source embodiments orchestrate multiple controllers with virtual head-height equalization and verified handoffs. It performs pre-infusion checks, logs uncertainty, and supports tilt compensation too. Continuous stream detection triggers alarms.