Noninvasive Glucose Sensing With EM and Pressure Feedback
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Solution Overview
Problem
Existing blood-glucose monitoring techniques, both invasive and non-invasive, face challenges such as limited measurement sensitivity, discomfort, pain, risk of infection, and difficulty in achieving accurate and consistent glucose concentration readings due to various factors affecting the response signal.
Innovation Solution
A non-invasive method using electromagnetic (EM) signals interacting with the body to determine glucose concentration, supplemented with pressure measurements, employs a predictive model trained to infer glucose levels accurately by accounting for device attachment and environmental factors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If non-invasive EM signal measurement is used, then comfort and ease of operation are improved, but measurement precision deteriorates due to signal response being affected by multiple factors
Solution Approach 1:
The system continuously monitors the EM signal response and uses feedback loops to adjust measurements in real-time, compensating for variations in signal response caused by device attachment pressure, body part anatomy, and motion. This feedback mechanism enables the system to maintain measurement precision despite the non-invasive measurement approach.
Solution Approach 2:
The invention changes the measurement parameters by incorporating multiple signal characteristics (amplitude, frequency, phase) and combining them with pressure sensor data and machine learning algorithms. This multi-parameter approach allows the system to extract accurate glucose concentration information from the EM signal responses that would otherwise be insufficient.
2Measurement precision
If repeated blood withdrawal procedures are performed, then measurement precision is improved through multiple samples, but loss of time and ease of operation deteriorate
Solution Approach 1:
The EM signal-based system enables continuous or near-continuous glucose monitoring without requiring repeated blood withdrawal procedures. The device can perform measurements continuously over time, providing ongoing glucose concentration data without interrupting the patient's routine, thus eliminating the time loss associated with repeated finger pricks.
3Duration of action of moving object
If invasive monitoring devices are implanted, then continuous measurement capability is improved, but device complexity and ease of operation deteriorate due to complex medical procedure requirements
Solution Approach 1:
The invention replaces complex mechanical implantable devices with a simpler external EM signal-based system. Instead of requiring surgical implantation of monitoring devices, the system uses non-invasive EM signals combined with pressure sensors and machine learning algorithms to achieve continuous glucose monitoring, thereby reducing the complexity of the medical procedure while maintaining continuous measurement capability.
4Device complexity
If EM signal measurement without pressure data is used, then device complexity is reduced, but measurement precision deteriorates due to inability to account for attachment variations
Solution Approach 1:
The invention merges the EM signal measurement system with pressure sensing capability, combining these two measurement approaches to achieve both continuous monitoring and high precision. The pressure sensor data is integrated with the EM signal responses through machine learning algorithms, creating a synergistic effect that improves measurement accuracy without requiring excessive complexity.
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
Provides accurate and consistent glucose concentration readings by improving measurement accuracy through the use of a predictive model that incorporates pressure data, ensuring secure device attachment and minimizing noise interference.
Implementation Method 1
acquiring first measurement data indicative of a response resulting from an electromagnetic (EM) signal interacting with the subject's blood in a body part of the subject
Data Source
AI summary
According to an aspect, there is provided a computer-implemented method (200) of determining a concentration of glucose in a subject's blood, the method comprising acquiring (202) first measurement data indicative of a response resulting from an electromagnetic (EM) signal interacting with the subject's blood in a body part of the subject, the EM signal having been emitted from a device in contact with the body part of the subject; acquiring (204) second measurement data indicative of a pressure applied to the body part of the subject by the device; and using (206) a predictive model to infer a concentration of glucose in the subject's blood from the first measurement data and the second measurement data, the predictive model having been trained to infer a concentration of glucose in the subject's blood from the first measurement data and the second measurement data.