Neurostimulation Device Personalization via Machine Learning
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Solution Overview
Problem
Current neuromodulation systems for treating disorders and symptoms are limited by their size, comfort, and ability to personalize therapeutic protocols effectively.
Innovation Solution
The development of compact, ergonomic neuromodulation devices that employ machine learning algorithms to optimize therapy parameters based on individual patient responses, using data from multiple patients to refine therapy profiles and adjust parameters over time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If machine learning algorithms are implemented to personalize therapy parameters, then treatment efficacy and patient satisfaction improve, but device complexity increases
Solution Approach 1:
The patent introduces machine learning algorithms as an intermediary component that processes patient response data and automatically adjusts therapy parameters. This mediator bridges the gap between raw patient data and optimized treatment protocols, enabling personalization without requiring complex manual programming or intervention. The algorithm acts as an intelligent intermediary that learns from data and makes therapeutic decisions.
Solution Approach 2:
The system implements self-service through automated therapy parameter optimization. The machine learning algorithm continuously monitors patient responses and automatically adjusts therapy settings without requiring manual intervention from clinicians. This self-adjusting capability enables the device to personalize treatment autonomously based on real-time patient feedback, improving efficacy while reducing operational complexity.
2Adaptability or versatility
If data collection from multiple patients is aggregated to refine therapy profiles, then adaptability of treatment protocols improves, but loss of information increases due to data aggregation
Solution Approach 1:
The patent applies local quality by maintaining patient-specific therapy profiles that capture individual characteristics and responses. While aggregating data across multiple patients to identify patterns and refine overall treatment protocols, the system preserves local patient-specific information through individualized profile storage. This ensures that generalizations from aggregation do not overwrite unique patient characteristics, balancing adaptability with information retention.
3Ease of operation
If compact and ergonomic device form factors are used, then ease of operation and comfort improve, but device functionality may be limited
Solution Approach 1:
The patent replaces complex mechanical or hardware-based functionality with software-based machine learning algorithms. By implementing therapy optimization through computational methods rather than physical mechanisms, the device achieves advanced adaptability and personalization capabilities within a compact form factor. This substitution enables rich functionality without increasing physical size, maintaining ease of operation while enhancing versatility.
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
These systems enhance treatment efficacy, compliance, and comfort by personalizing therapy protocols, improving patient satisfaction, and optimizing therapy outcomes based on individual kinematic and satisfaction data.
Implementation Method 1
one or more electrodes configured to generate electric stimulation signals based on therapy parameters
Data Source
AI summary
Systems, devices, and methods for electrically stimulating peripheral nerve(s) to treat various disorders are disclosed, as well as signal processing systems and methods for enhancing diagnostic and therapeutic protocols relating to the same. Personalized therapy based on algorithms and aggregated data are also provided.