Insulin Pump Augmentation System with Motion Sensors
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Solution Overview
Problem
Conventional insulin pump systems fail to adequately detect and compensate for changing physiological, lifestyle, and exercise-related factors, leading to unpredictable blood glucose levels in diabetic individuals.
Innovation Solution
A controller-based system that utilizes sensors such as accelerometers and gyroscopic pitch sensors to detect lifestyle events and adjust insulin delivery rates accordingly, providing a closed-loop insulin pump augmentation system (IPAS) for dynamic and proactive insulin management.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional insulin pump systems use rigid time-based delivery schedules, then the system operation is simple and reliable, but the system cannot adapt to changing physiological, lifestyle, and exercise conditions
Solution Approach 1:
The patent implements feedback mechanisms by continuously monitoring glucose levels, motion data, and other physiological parameters to dynamically adjust insulin delivery rates. The system uses sensor feedback from accelerometers and gyroscopes to detect lifestyle events and modifies pump operation accordingly, creating a closed-loop control system that adapts to real-time conditions.
Solution Approach 2:
The system transitions from static, time-based delivery schedules to dynamic, real-time adjustment of insulin delivery rates. The pump continuously modifies delivery parameters based on detected lifestyle events, glucose trends, and physiological data, enabling adaptive response to changing user conditions rather than following fixed timetables.
2Reliability
If the system continuously monitors and adjusts insulin delivery in real-time, then blood glucose control improves, but energy consumption and device complexity increase
Solution Approach 1:
The system employs periodic sampling of sensor data at optimized intervals rather than continuous monitoring, balancing data collection needs with energy conservation. The monitoring frequency adjusts based on glucose variability, lifestyle event detection, and current physiological state, reducing unnecessary sensor readings during stable periods while increasing frequency when needed.
Solution Approach 2:
The system performs preliminary actions by predicting glucose trends based on historical data patterns, lifestyle event probability assessment, and physiological modeling before actual glucose excursions occur. This predictive approach allows proactive insulin adjustment rather than reactive correction, improving control reliability while reducing the intensity of continuous monitoring requirements.
3Reliability
If the system reacts only after glucose deviations occur, then the control logic is simple, but the system cannot prevent harmful glucose excursions
Solution Approach 1:
The system performs preliminary assessment of glucose trends, motion patterns, and lifestyle event probabilities to predict future glucose excursions before they occur. By analyzing rate-of-change data, historical patterns, and contextual information, the system proactively adjusts insulin delivery to prevent hyperglycemia and hypoglycemia rather than reacting after deviations manifest.
Solution Approach 2:
The system implements multi-layer feedback mechanisms that continuously monitor glucose trends, motion data, and physiological parameters to detect early signs of impending glucose excursions. The feedback loop processes real-time data to identify patterns预示着 future deviations and triggers preemptive insulin adjustment, creating a proactive control system.
4Measurement precision
If the system uses multiple sensors and real-time monitoring, then lifestyle event detection accuracy improves, but device complexity and manufacturing cost increase
Solution Approach 1:
The patent merges multiple sensor types (accelerometers, gyroscopes, glucose sensors) into an integrated pump system with unified data processing. The combined sensor data streams are processed through centralized algorithms that correlate motion patterns, glucose trends, and contextual information to accurately identify lifestyle events, achieving high detection accuracy through synergistic integration rather than separate independent systems.
Solution Approach 2:
The sensor system is designed with multi-functionality, where the same accelerometers and gyroscopes serve multiple purposes: detecting lifestyle events, monitoring physical activity levels, identifying sleep patterns, and providing data for predictive glucose modeling. This universal sensor framework reduces overall system complexity compared to dedicated single-function sensor systems.
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 effectively maintains blood glucose levels within a target range by dynamically adjusting insulin delivery based on real-time lifestyle event detection, reducing the risk of both hyperglycemia and hypoglycemia.
Implementation Method 1
an accelerometer sensor arranged on the body and configured to output motion data based on detected motion
Implementation Method 2
a gyroscopic pitch sensor arranged on the body and configured to output orientation data based on detected orientation
Data Source
AI summary
A controller and associated multi-axis sensor system for augmenting the automatic intelligent delivery of one or more drugs is provided. The controller and associated multi-axial sensor system are based on the detection and determination of particular physical lifestyle events. As a specific example, a pump augmentation system includes a six-axis accelerometer sensor, a gyroscopic pitch sensor and a controller. The controller is configured to receive motion data from the six-axis accelerometer sensor and orientation data from the gyroscopic pitch sensor. The controller provides a pump instruction signal for changing a delivery rate of a drug to a user based on the motion data and the orientation data. The system and methods are particularly suited for treating a user with Parkinson's disease.