Multi-Sensor Gesture Detection for Automated Insulin Delivery
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Solution Overview
Problem
Existing medication delivery systems, such as insulin infusion devices, struggle to account for variations in patient insulin response due to daily activities and meal consumption, leading to manual errors and reduced therapy effectiveness.
Innovation Solution
A gesture-informed patient management system that uses multiple sensor arrangements to detect patient movements and activities, predicting events to automatically adjust medication delivery based on these detections.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual insulin administration is used, then patient control over therapy is maintained, but manual errors occur and patient workload increases
Solution Approach 1:
The system enables self-service by automatically detecting patient activities (meals, exercise, stress) through sensors and autonomously adjusting insulin delivery without requiring manual patient input. The pump monitors physiological parameters and lifestyle events, then self-regulates medication dosing based on detected patterns, freeing the patient from manual calculation and administration tasks while maintaining reliable glucose control.
Solution Approach 2:
The system implements continuous feedback loops where sensors monitor glucose levels, activity patterns, and physiological parameters, which are then processed by the control algorithm to automatically adjust insulin delivery. This closed-loop feedback mechanism ensures therapy effectiveness is maintained while reducing manual patient intervention, as the system continuously adapts to changing patient needs based on real-time data.
2Reliability
If automated medication delivery is implemented, then therapy effectiveness improves, but device complexity increases
Solution Approach 1:
The system achieves multi-functionality by integrating multiple sensor types (accelerometers, gyroscopes, glucose sensors), activity detection algorithms, and automated insulin delivery into a single unified platform. This universal device performs glucose monitoring, activity tracking, event detection, and medication delivery simultaneously, improving therapy effectiveness while consolidating complexity into one coordinated system rather than multiple separate devices.
Solution Approach 2:
The patent merges previously separate functions (manual insulin dosing, glucose monitoring, activity tracking) into an integrated automated system. By combining the medication pump, sensors, and control algorithms into a unified device that operates autonomously, the system improves reliability through consistent automated delivery while managing complexity through integrated design rather than coordinated multiple devices.
3Measurement precision
If multiple sensor arrangements are used, then event detection accuracy improves, but device complexity increases
Solution Approach 1:
The system applies segmentation by dividing the sensing function into multiple specialized sensor arrangements positioned at different locations (wrist, finger, palm). Each sensor array detects specific gesture components, and the control algorithm integrates these segmented measurements to achieve high overall detection accuracy. This modular segmentation improves measurement precision while managing complexity through distributed, specialized sensing rather than a single complex sensor.
Data Source
AI summary
Gesture-informed patient management systems and related medical devices and operating methods are provided. A method of operating a medical device capable of influencing a physiological condition of a patient involves obtaining first sensor measurement data from a sensing arrangement associated with a first location on a body of the patient and capable of detecting physical movement by the patient, obtaining second sensor measurement data from a second sensing arrangement having a second location different from the first location, predicting an occurrence of an event based at least in part on the first sensor measurement data in a manner that is influenced by the second sensor measurement data, resulting in a predicted occurrence of the event, and automatically configuring operation of the medical device to influence the physiological condition of the patient in a manner that is influenced by the predicted occurrence of the event.