Closed-Loop Symptom Intervention for Adaptive Neuromodulation
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Solution Overview
Problem
Existing treatments for conditions like Parkinson's disease struggle with unpredictable symptom fluctuations, poor temporal resolution in clinical assessments, and undesirable side effects, leading to challenges in titrating chemical stimuli and personalizing symptom management.
Innovation Solution
A symptom intervention system using machine learning and neuromodulation to monitor and optimize chemical stimulus administration and neuromuscular activity, employing on-body sensors and wearable devices for continuous symptom monitoring and actuation, with personalized actuation instructions based on user-specific models.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If chemical stimuli like l-DOPA are administered to treat Parkinson's disease, then motor function is restored, but dosage titration becomes unpredictable and side effects occur
Solution Approach 1:
The system continuously monitors patient symptoms using sensors and provides feedback to an AI agent that adjusts chemical stimulus administration in real-time. This closed-loop feedback mechanism enables precise dosage titration by responding to actual symptom severity rather than relying on predetermined schedules, thereby improving reliability while managing complexity through automated decision-making.
Solution Approach 2:
The system enables the patient's own symptom data to drive treatment adjustments through AI processing. The AI agent analyzes real-time symptom monitoring data and autonomously determines optimal dosing decisions, allowing the treatment system to self-regulate based on individual patient response patterns without requiring manual clinician intervention for each adjustment.
2Measurement precision
If clinical exams are used to assess patient symptoms, then standardized evaluation is provided, but temporal resolution is poor and real-world severity is not captured
Solution Approach 1:
The system replaces intermittent clinical exams with continuous symptom monitoring through wearable sensors that operate 24/7. This continuous data collection captures symptom fluctuations throughout the day, including during real-world activities, providing both high temporal resolution and ecologically valid measurements of disease severity without requiring scheduled appointments.
Solution Approach 2:
The system substitutes manual clinical examination with automated electronic sensing and AI analysis. Instead of relying on visual assessment by clinicians during brief visits, the system uses sensors to objectively measure symptom parameters and an AI agent to interpret the data, enabling precise, continuous, and automated symptom characterization that captures both temporal dynamics and real-world context.
3Reliability
If standardized rating scales are used to evaluate patient conditions, then objective scoring is achieved, but subjective nature and poor temporal resolution limit real-world applicability
Solution Approach 1:
The system transitions from static, fixed-point clinical assessments to dynamic, continuous symptom tracking that adapts to real-time changes in patient condition. The AI agent processes streaming sensor data to characterize symptom severity at any moment, providing objective measures that naturally adapt to varying contexts, activities, and temporal patterns without requiring standardized exam protocols.
Solution Approach 2:
The system creates a universal symptom characterization framework that works across diverse real-world scenarios rather than being limited to specific clinical exam contexts. By continuously monitoring multiple symptom dimensions through various sensors during everyday activities, the system provides objective, adaptable assessments that can be applied universally across different patients, settings, and temporal conditions.
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
Enhances precision in titrating chemical stimuli, improves mobility, and personalizes symptom management by adapting to individual user needs, reducing symptom severity and side effects through closed-loop monitoring and neuromodulation.
Implementation Method 1
On-body sensors provide continuous measurements of symptom state
Implementation Method 2
The assembly can stimulate the neuromuscular system to directly augment movements and improve mobility through neuromodulation or functional electrical stimulation (FES)
Implementation Method 3
The assembly can stimulate the neuromuscular system to directly augment movements and improve mobility through neuromodulation or functional electrical stimulation (FES)
Data Source
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AI summary
A symptom intervention system monitors data representative of a user's movement, identifies an onset of a symptom of a physical condition, and applies an actuation to intervene with the identified onset. A machine-learned model is trained to identify an onset of a symptom based on the monitored data. The system may use the machine-learned model to determine whether to modify an upcoming administration of a chemical stimulus that is administered to the user to treat their physical condition. The system may determine a modification to a dose or a time of the upcoming administration of the stimulus and apply the stimulus to the user based on the determined modification. The system may use the machine-learned model to determine that the user is exhibiting a particular symptom of their physical condition. Depending on the symptom and user, the system determines a neuromodulation operation and applies the operation to the user.