Variable Data Usage Personal Medical System for Diabetes Management
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Solution Overview
Problem
Wearable medical devices for diabetes management transmit data too frequently, leading to high cellular network costs and battery depletion, and result in unreliable data due to infrequent updates, causing potential misdiagnosis and insufficient alarms for corrective action.
Innovation Solution
A variable data usage system that allows self-care devices to transmit data at fixed intervals, with a cellular communication device storing and counting data packets, and a cloud infrastructure controlling the interval index to optimize data transmission, reducing frequency and conserving resources while maintaining data accuracy and alarm effectiveness.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If data is transmitted at a regular frequency, then data currentness is improved, but cellular network cost and battery consumption increase
Solution Approach 1:
The system dynamically adjusts the data transmission interval based on glucose data stability. When glucose levels are stable, the transmission interval increases (reducing frequency). When glucose levels change rapidly, the transmission interval decreases (increasing frequency). This dynamic adjustment resolves the contradiction by making transmission frequency adaptive rather than fixed.
Solution Approach 2:
The system changes the transmission interval parameter based on the stability parameter of glucose data. The processor calculates stability by comparing consecutive glucose readings and adjusts the transmission interval accordingly. This parameter change approach allows the system to optimize between data currentness and energy consumption by modifying the transmission frequency parameter based on actual data conditions.
2Reliability
If data is transmitted at a regular frequency, then data currentness is improved, but cellular network cost increases
Solution Approach 1:
The system dynamically adjusts the data transmission interval based on glucose data stability. When glucose levels are stable, the transmission interval increases (reducing frequency and cost). When glucose levels change rapidly, the transmission interval decreases (increasing frequency and cost only when necessary). This dynamic adjustment resolves the contradiction by making transmission frequency adaptive rather than fixed.
Solution Approach 2:
The system changes the transmission interval parameter based on the stability parameter of glucose data. The processor calculates stability by comparing consecutive glucose readings and adjusts the transmission interval accordingly. This parameter change approach allows the system to optimize between data currentness and network cost by modifying the transmission frequency parameter based on actual data conditions.
3Loss of energy
If data transmission interval is increased to reduce cost, then battery consumption is reduced, but data currentness deteriorates
Solution Approach 1:
The system dynamically adjusts the data transmission interval based on glucose data stability. When glucose levels are stable, the transmission interval increases (reducing frequency). When glucose levels change rapidly, the transmission interval decreases (increasing frequency). This dynamic adjustment resolves the contradiction by making transmission frequency adaptive rather than fixed.
Solution Approach 2:
The system changes the transmission interval parameter based on the stability parameter of glucose data. The processor calculates stability by comparing consecutive glucose readings and adjusts the transmission interval accordingly. This parameter change approach allows the system to optimize between data currentness and energy consumption by modifying the transmission frequency parameter based on actual data conditions.
4Loss of energy
If data transmission interval is increased to reduce cost, then battery consumption is reduced, but data reliability deteriorates
Solution Approach 1:
The system dynamically adjusts the data transmission interval based on glucose data stability. When glucose levels are stable, the transmission interval increases (reducing frequency). When glucose levels change rapidly, the transmission interval decreases (increasing frequency). This dynamic adjustment resolves the contradiction by making transmission frequency adaptive rather than fixed.
Solution Approach 2:
The system changes the transmission interval parameter based on the stability parameter of glucose data. The processor calculates stability by comparing consecutive glucose readings and adjusts the transmission interval accordingly. This parameter change approach allows the system to optimize between data currentness and energy consumption by modifying the transmission frequency parameter based on actual data conditions.
Data Source
AI summary
A variable data usage personal medical system including a self-care device attached to a patient, and operable to generate self-care device data and to transmit the self-care device data at a fixed interval; a cellular communication device operable to receive and store the transmitted self-care device data, to register a count at an interval counter for each of the fixed intervals in which the transmitted self-care device data is received, to generate a data cellular packet from overhead plus the stored self-care device data when the interval counter equals a fixed interval index, and to transmit the data packet; and a cloud infrastructure operably connected to the cellular communication device over a cellular network, and operable to receive, process, and store the transmitted data packet. The cloud infrastructure is operable to transmit a value for the fixed interval index to the cellular communication device for storage.