Biosensor CNN for Non-Invasive Glucose and Blood Pressure
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Solution Overview
Problem
Traditional methods for measuring blood glucose and blood pressure are invasive, uncomfortable, and limited in real-time monitoring capabilities, while non-invasive techniques face challenges such as complex calibration, accuracy issues due to skin pigmentation and ambient light, and limited accuracy from tissue property variations.
Innovation Solution
A biosensor system that combines physiological signals analysis with Convolutional Neural Network (CNN) modeling, using a biosensor module to capture and preprocess signals, and a CNN module to measure and predict blood glucose and blood pressure levels, providing a non-invasive and user-friendly monitoring solution.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional invasive methods (finger pricking, cuff-based devices) are used for measurement, then measurement accuracy is improved, but user comfort and convenience deteriorate
Solution Approach 1:
The patent replaces mechanical invasive measurement systems (finger pricking devices, cuff-based blood pressure monitors) with an optical non-invasive system. The optical sensor captures physiological signals through light transmission through the finger, eliminating the need for blood sampling and mechanical cuffs, thus improving user comfort while maintaining measurement capability
Solution Approach 2:
The patent introduces an intermediary processing system that includes a microcontroller unit, processor, and display unit between the optical sensor and the final measurement output. This intermediary system processes the raw optical signals, applies calibration algorithms, and derives the physiological parameters, enabling accurate non-invasive measurement without direct tissue penetration
2Ease of operation
If non-invasive optical spectroscopy techniques are used, then user comfort is improved, but measurement accuracy deteriorates due to skin pigmentation and ambient light influences
Solution Approach 1:
The patent applies preliminary calibration action by storing calibration data in memory before actual measurements are taken. The system uses calibration signals to establish baseline optical properties for each user, creating a reference framework that compensates for individual variations in skin pigmentation and tissue characteristics before physiological measurements are performed
Solution Approach 2:
The patent implements feedback mechanisms where the microcontroller unit continuously processes optical signals and adjusts measurements based on real-time signal quality assessment. The system monitors signal strength and characteristics, comparing them against expected physiological ranges, and can request re-measurement or apply correction algorithms when signal quality is compromised by ambient light or tissue variations
3Ease of operation
If impedance-based measurements are used, then non-invasive monitoring is achieved, but accuracy is limited due to tissue property variations
Solution Approach 1:
The patent changes the fundamental measurement parameter from electrical impedance to optical transmission properties. Instead of measuring electrical resistance through tissues (which varies with composition and hydration), the system measures light transmission characteristics that are less sensitive to these variations, thereby improving measurement accuracy while maintaining non-invasive operation
4Productivity
If pulse wave analysis is used for continuous monitoring, then real-time monitoring capability is improved, but measurement accuracy deteriorates due to reliance on calibration against cuff-based measurements
Solution Approach 1:
The patent enables the system to perform self-calibration by using the stored calibration data and real-time signal processing to automatically adjust measurements without requiring external cuff-based devices. The microcontroller unit continuously refines measurements based on the optical signals and compares them against physiological norms, enabling independent accurate monitoring without external calibration equipment
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 achieves accurate and reliable non-invasive measurement and prediction of blood glucose and blood pressure levels in real-time, enhancing user comfort and convenience while improving healthcare management.
Implementation Method 1
Optical spectroscopy techniques have been employed to capture physiological signals and analyze blood components using light absorption or scattering properties
Implementation Method 2
A biosensor system for non-invasive measurement and prediction of blood glucose and blood pressure levels is disclosed. The biosensor system comprises of a biosensor module configured to capture physiological signals and preprocess the captured signals to eliminate noise and optimize for subsequent analysis
Implementation Method 3
Convolutional Neural Networks (CNNs) have been successfully applied in various healthcare applications, including medical image analysis and physiological signal processing. These deep learning techniques extracts features from input signals and make accurate predictions based on the learned patterns and relationships within the data
Data Source
AI summary
The present invention discloses a biosensor system designed for non-invasive measurement and prediction of blood glucose and blood pressure levels. The system comprises a biosensor module, a processing module, a convolutional neural network (CNN) module, a display module, a user interface module, and a control module. The biosensor module incorporates optical spectroscopic techniques to capture physiological signals, which are preprocessed to eliminate noise and optimize for subsequent analysis. A scalogram image is generated from the preprocessed signals, and the processing module further processes the image. The CNN module utilizes the processed scalogram image to accurately measure and predict blood glucose and blood pressure levels. The system offers a user-friendly interface displayed on a screen, enabling users to interact and view the predicted results. The proposed method is non-invasive, relying on capturing and analyzing the physiological signals to provide reliable and convenient monitoring of blood glucose and blood pressure levels.