Controller Interface for Implantable Stimulator Device Configuration
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Solution Overview
Problem
Current implanted stimulator devices lack an efficient method for configuring optimal stimulation settings, such as polarity and pulse parameters, to minimize pain and maximize therapeutic effectiveness, often requiring manual adjustment and lacking personalized feedback mechanisms.
Innovation Solution
A computer-assisted method and controller device that present configuration options to users, receive feedback on pain levels, build user profiles, and adjust settings based on historical data to optimize stimulation parameters like polarity, pulse rate, and amplitude, using a learning engine to automate parameter selection for improved pain relief.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual adjustment of stimulation parameters is used, then device complexity is reduced, but ease of operation deteriorates due to requiring user expertise and time-consuming trial-and-error configuration
Solution Approach 1:
The system performs self-configuration by automatically analyzing user feedback (pain levels, comfort) and adjusting stimulation parameters without requiring manual intervention. The controller device autonomously builds user profiles and selects optimal configuration options, allowing the device to serve itself in the configuration process.
Solution Approach 2:
The system continuously receives feedback from users about their pain levels and comfort during stimulation, then uses this feedback to automatically adjust parameters. The feedback loop enables the system to learn from user responses and refine configuration decisions, improving ease of operation while managing complexity through intelligent algorithms.
2Adaptability or versatility
If personalized configuration is implemented, then adaptability improves, but device complexity increases due to requiring user profiles and learning algorithms
Solution Approach 1:
The system performs preliminary actions by pre-building user profiles based on initial feedback and pre-selecting configuration options for future treatments. The controller device stores user profiles and uses them to automatically configure optimal parameters before each treatment session, enabling personalized adaptability while reducing real-time decision complexity.
Solution Approach 2:
The system autonomously manages personalization by automatically creating and updating user profiles without requiring manual input. The controller device self-manages the complexity of personalized configuration through intelligent algorithms that learn from feedback and automatically select optimal parameters tailored to each user.
3Productivity
If automated parameter selection is used, then productivity improves by reducing configuration time, but device complexity increases due to requiring learning engines and feedback processing
Solution Approach 1:
The system uses feedback from user responses to automatically adjust parameters and improve configuration efficiency. By processing feedback data and learning from user experiences, the system rapidly converges on optimal settings, significantly reducing configuration time while managing complexity through efficient algorithms.
Solution Approach 2:
The controller device autonomously performs parameter selection and optimization without requiring manual configuration. The system self-manages the complex tasks of analyzing feedback, building profiles, and selecting parameters, thereby improving productivity while containing complexity within the automated system rather than requiring user expertise.
4Measurement precision
If user feedback collection is implemented, then measurement precision improves for pain assessment, but device complexity increases due to requiring feedback processing systems
Solution Approach 1:
The system implements structured feedback collection where users report pain levels and comfort during stimulation. This feedback mechanism enables precise measurement of treatment effectiveness by systematically capturing user responses and using them to objectively assess pain relief, transforming subjective experience into measurable data for optimization.
Data Source
AI summary
Some computer-assisted methods include: presenting configuration options to a user of the implanted stimulator device, the configuration options comprising stimulation parameters for the implanted stimulator; receiving a user specification of the configuration options in response to the presented configuration options; receiving user feedback when the user specified configuration options are implemented at the implanted stimulator device, the user feedback comprising a quantitative index of pain resulting from implementing the user specified configuration options on the implanted stimulator device; building a user profile for the user based on the user specified configuration options and the user feedback, the user profile including the user specified configuration options as well as the corresponding quantitative index of pain; and selecting at least one configuration option based on the user profile when the configuration options are subsequently presented to the user for a later treatment.