Wearable Incontinence Prediction Using Electrical Impedance Tomography
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Solution Overview
Problem
Current solutions fail to effectively predict and manage urinary incontinence, leading to discomfort, skin irritation, infections, and reduced quality of life for millions of individuals, particularly in the elderly and those in nursing facilities, due to lack of accurate and non-invasive monitoring systems.
Innovation Solution
A wearable incontinence prediction system utilizing electrical impedance tomography (EIT) devices with electrodes on the lower abdomen, posture detectors, and machine learning algorithms to analyze bladder status, posture, and activity, predicting incontinence risk and notifying users or caregivers of high-risk points.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional incontinence monitoring methods are used, then the system is simple to implement, but the prediction accuracy and reliability are insufficient
Solution Approach 1:
The patent combines multiple monitoring functions (bladder volume detection via EIT, posture detection via accelerometers, activity recognition via machine learning) into a single integrated wearable system. This merging approach improves prediction accuracy by synthesizing multiple data sources while managing complexity through unified system architecture and centralized processing.
Solution Approach 2:
The wearable device performs multiple functions simultaneously: electrical impedance tomography for bladder monitoring, accelerometer-based posture detection, machine learning-based activity recognition, and predictive analytics. This multi-functionality enables comprehensive incontinence prediction while the system manages complexity through modular design and integrated processing.
2Object-affected harmful factors
If non-invasive EIT monitoring is implemented, then patient comfort and skin health are improved, but the device complexity and measurement requirements increase
Solution Approach 1:
The patent replaces invasive mechanical catheter-based monitoring with non-invasive electrical impedance tomography. This substitution eliminates skin irritation and infection risks associated with invasive methods while using electromagnetic fields to detect bladder volume. The complexity is managed through wearable electrode arrays and sophisticated signal processing algorithms.
Solution Approach 2:
The EIT system uses electrical impedance as an intermediary measurement to indirectly detect bladder volume without direct contact with urine or invasive probes. This intermediary approach provides non-invasive monitoring while the complexity is handled through multi-electrode arrays and tomographic reconstruction algorithms.
3Reliability
If real-time multi-parameter monitoring is performed, then the incontinence prediction reliability is improved, but the energy consumption and processing requirements increase
Solution Approach 1:
The system performs monitoring and processing in periodic cycles rather than continuous operation. EIT measurements, posture detection, and machine learning inference are executed at optimized intervals based on bladder filling rates and user activity patterns. This periodic approach maintains prediction reliability while significantly reducing average power consumption compared to continuous monitoring.
Solution Approach 2:
The machine learning model performs preliminary analysis of EIT and posture data to predict incontinence risk before actual incontinence events occur. This advance prediction allows the system to notify users proactively, improving reliability by providing early warnings while reducing energy consumption by avoiding continuous high-power processing.
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 accurate, non-invasive prediction of incontinence risk, improving patient outcomes by enabling timely interventions and enhancing quality of life through personalized management plans.
Implementation Method 1
an electrical impedance tomography (EIT) device that includes a plurality of electrodes configured to be worn at a lower abdomen of a user around the bladder. A first two or more electrodes are configured to apply alternating currents to a body of the user, and a second two or more electrodes record resulting potentials detected in the body of the user.
Data Source
AI summary
A system for incontinence prediction includes an electrical impedance tomography (EIT) device, a posture detector, and one or more processors communicatively coupled to the EIT device and the posture detector. The EIT device may include a plurality of electrodes configured to be worn at a lower abdomen of a user around the bladder. The one or more processors is configured to collect an EIT image generated by the EIT device, a posture generated by the posture detector, and a planning activity of the user. The one or more processors may further determine a bladder status based on the EIT image of the user, predict an incontinence risk based on the bladder status, the posture, and the planning activity of the user and determine a high-risk point indicating a high incontinence risk, and notify the user or a caregiver of the high incontinence risk at the high-risk point.