mTBI Screening via Dynamic Motor Tracking Error Modeling
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Solution Overview
Problem
Current methods for diagnosing mild traumatic brain injury (mTBI) are unreliable and impractical for immediate screening, especially in field settings, as they often rely on subjective questionnaires and require extensive testing, which can delay detection and increase the risk of further brain damage.
Innovation Solution
A system that uses dynamic motor tracking tasks to gather data on a subject's tracking errors, which are then used to generate numerical values for model parameters, compared to known diagnoses to accurately screen for mTBI using a classification model, allowing for rapid and reliable diagnosis in minutes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If standard questionnaires and self-reporting methods are used for mTBI screening, then the screening can be administered immediately after injury, but the reliability and accuracy of diagnosis is poor
Solution Approach 1:
The patent replaces subjective questionnaire-based assessment with objective motor performance measurement using a motor tracking device. The system captures precise motor responses (position, velocity, acceleration) and uses computational modeling to detect mTBI, substituting mechanical/physical measurement for subjective self-reporting.
Solution Approach 2:
The patent introduces a computational model as an intermediary between raw motor tracking data and diagnosis. The model processes tracking errors and generates model values that serve as an objective bridge between observable motor behavior and underlying brain injury status, improving both reliability and precision.
2Measurement precision
If detailed neurological evaluations and extensive cognitive testing are performed, then diagnostic accuracy improves, but time consumption and complexity increase significantly
Solution Approach 1:
The patent extracts only the essential motor tracking component needed for mTBI detection, separating it from comprehensive neurological evaluations. By focusing specifically on motor performance metrics and their relationship to brain injury, the system achieves high diagnostic accuracy with minimal time investment.
Solution Approach 2:
The patent segments the diagnostic process into distinct components: motor tracking data collection, error calculation, model value generation, and classification. This segmentation allows the system to perform only the necessary steps for mTBI detection rather than conducting full neurological evaluations, reducing time while maintaining accuracy.
3Measurement precision
If comprehensive neurological evaluations are conducted, then diagnostic accuracy improves, but device complexity and operational burden increase
Solution Approach 1:
The patent designs a motor tracking device that can serve multiple functions: capturing position data, calculating tracking errors, generating model values, and supporting diagnosis. This multi-functionality reduces the need for multiple separate devices and procedures, simplifying the overall system while maintaining diagnostic accuracy.
4Ease of operation
If standard questionnaires are used for immediate screening, then the screening can be performed in the field, but the results are often unreliable and impractical
Solution Approach 1:
The motor tracking device enables self-administered testing where the subject performs the tracking task independently while the system automatically captures data, calculates errors, and generates results. This eliminates the need for trained personnel to administer complex questionnaires, improving both ease of operation and reliability in field settings.
Data Source
AI summary
The disclosure provides for easy, reliable, and rapid screening of a mild traumatic brain injury (mTBI) based on a modeling of a subject's tracking of a dynamic target during the course of a simple motor tracking task. The gathered tracking data can be used to calculate tracking errors between the subject's actual input (e.g., grip force) and the intended target input. The tracking errors may be used to generate numerical values for model parameters that correlate the subject's responses to the tracking errors during the course of the dynamic motor tracking task. A classification model may be used to compare the model values to multi-subject model values of known diagnoses for mTBI. The entire screening process can be effectively administered in a matter of minutes or less, and with a high degree of accuracy.