Machine Learning Communication Timing for Implantable Device Battery Life
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Solution Overview
Problem
Medical devices, particularly implantable medical devices (IMDs), face challenges in extending battery life and ensuring successful communication due to unnecessary power drain from frequent, unsuccessful attempts to communicate with external devices when they are out of range.
Innovation Solution
Implementing machine learning techniques to identify time periods with higher likelihood of successful communication based on sensed parameters, allowing devices to advertise for communication only during these times and refrain from advertising during less likely periods.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the IMD advertises for communication at predetermined intervals, then communication opportunities are increased, but power source longevity deteriorates due to unnecessary power drain from unsuccessful communication attempts
Solution Approach 1:
The system dynamically adjusts the advertising interval based on detected patterns in external device presence. Instead of using fixed predetermined intervals, the IMD modifies its communication advertising behavior in real-time based on learned patterns, reducing power consumption while maintaining communication reliability.
Solution Approach 2:
The IMD uses its own sensed parameters and communication outcomes to learn and adapt its advertising schedule autonomously. The device self-optimizes its communication strategy by analyzing patterns in its own operation data, eliminating the need for external configuration or manual intervention.
2Reliability
If the IMD communicates frequently with external devices, then communication reliability is improved, but energy consumption increases reducing battery life
Solution Approach 1:
The system performs preliminary learning during an initial period to establish patterns of external device presence before implementing the optimized advertising schedule. This preliminary action allows the device to pre-determine optimal communication times based on historical data, avoiding unnecessary power consumption while maintaining reliability.
Solution Approach 2:
The advertising interval parameter is dynamically changed based on learned patterns. The system transitions from fixed intervals to variable intervals that adapt to actual communication needs, changing the timing parameter to optimize both reliability and energy consumption.
3Duration of action of moving object
If machine learning techniques are implemented to optimize communication timing, then battery life is extended, but device complexity increases
Solution Approach 1:
The system implements a simplified version of machine learning that focuses only on the essential pattern recognition needed for advertising optimization. Rather than implementing full-scale ML algorithms, it uses partial action by applying only the necessary computational techniques to achieve the desired battery life extension without excessive complexity.
Data Source
AI summary
This disclosure describes systems, devices and techniques for improving the longevity of battery life in a second device. An example first device includes communication circuitry configured to communicate with the second device and one or more sensors configured to sense parameters associated with the first device. The first device includes processing circuitry configured to determine a first time period when a likelihood of successful communications with the second device is higher than a second time period based on the sensed parameters, and control the communication circuitry to communicate with the second device during the first time period and refrain from communicating with the second device during the second time period.