Neurostimulation Therapy Management With Sensor-Guided Parameter Updates
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing neurostimulation systems, such as Spinal Cord Stimulation (SCS), face challenges in customization and optimization for individual patient needs, leading to underutilization and reliance on less effective therapies due to the complexity of programming and the need for timely updates in response to changing patient conditions.
Innovation Solution
A digital ecosystem linking patients to various users through an integrated coach mobile application, wearable sensors, and a device management system to optimize neurostimulation therapies by providing real-time guidance and adjustments based on patient-specific data and conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If neurostimulation therapy is customized for individual patient needs with sophisticated pulse patterns, then therapy effectiveness is improved, but device complexity and programming difficulty increase
Solution Approach 1:
The system enables patients to self-manage their neurostimulation therapy by providing them with mobile applications and wearable sensors that allow them to adjust parameters, monitor their own conditions, and receive real-time feedback without requiring complex programming expertise. This transfers the complexity management from patient to system, maintaining effectiveness while reducing the burden on users.
Solution Approach 2:
The system continuously collects patient-specific data through wearable sensors and mobile applications, analyzes this data to determine optimal stimulation parameters, and automatically adjusts therapy settings. This closed-loop feedback mechanism ensures therapy remains effective and customized to individual patient needs while eliminating the need for manual programming complexity.
2Reliability
If neurostimulation parameters are updated timely in response to changing patient conditions, then therapy effectiveness is maintained, but ease of operation decreases due to frequent adjustments needed
Solution Approach 1:
The system automatically monitors patient conditions through wearable sensors and mobile applications, and performs parameter updates without requiring manual user intervention. The patient simply wears the sensors and uses the application, while the system handles the complex task of analyzing data and adjusting parameters, maintaining ease of operation despite frequent updates.
Solution Approach 2:
The continuous feedback loop from wearable sensors provides real-time data on patient conditions, enabling the system to automatically detect when parameter adjustments are needed and implement them seamlessly. This maintains therapy effectiveness while keeping the user experience simple and easy to operate.
3Reliability
If sophisticated pulse patterns are used to emulate natural neural signals, then therapy effectiveness improves, but ease of operation worsens due to expertise requirements
Solution Approach 1:
The system performs the complex task of generating and adjusting sophisticated pulse patterns automatically based on patient data collected from wearable sensors. The patient does not need to understand or manually configure these complex patterns, as the system self-adjusts to emulate natural neural signals appropriate for each individual, maintaining effectiveness while ensuring ease of use.
Solution Approach 2:
The system uses real-time feedback from wearable sensors to automatically generate and adjust sophisticated pulse patterns that emulate natural neural signals. This closed-loop approach ensures the complex patterns are always optimized for the individual patient without requiring their expertise to program or adjust them.
4Adaptability or versatility
If patient-specific data collection and analysis are implemented, then adaptability to individual needs improves, but device complexity increases
Solution Approach 1:
The system divides the complexity of patient-specific data collection and analysis across multiple components: wearable sensors for data collection, mobile applications for data management and initial analysis, and cloud-based or device-based processing systems for advanced analysis and parameter determination. This segmentation distributes the complexity burden while enabling comprehensive customization capability.
Data Source
Figure 1~2
Figure 3~4
Figure 5
AI summary
An example of a neurostimulation system may include one or more sensing devices and a patient assistance device. The sensing device(s) may be configured to sense one or more signals from a patient and may include one or more non-invasive sensing devices. The patient assistance device may be configured to assist the patient in use of a stimulation device and may include a communication circuit configured to receive the sensed signal(s), a user interface configured to allow for interactions with the patient, and a processing circuit which may be configured to receive patient-specific information including the sensed signal(s), to analyze the patient-specific information with neurostimulation algorithm information representative of available therapeutic options, to produce one or more recommendations related to use of the stimulation device for treating the patient based on the analysis, and to present at least one recommendation using the user interface.