Wearable Brain Injury Estimation System
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current methods for determining brain injury, such as chronic traumatic encephalopathy (CTE) resulting from repetitive head impacts, often rely on clinical signs and symptoms, which may not appear until long-term damage is incurred, failing to detect sub-concussive or asymptomatic brain injuries in real-time.
Innovation Solution
A wearable technology system that uses sensors to measure forces applied to the head or body, coupled with machine learning models trained on MRI and MRE data, to estimate brain damage without requiring extensive imaging, providing real-time alerts for potential brain injury.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If clinical signs and symptoms are used to determine brain injury, then the diagnosis is reliable, but the detection is delayed until long-term damage is incurred
Solution Approach 1:
The system performs preliminary detection of brain injury by measuring forces applied to the head and estimating brain damage before clinical symptoms appear. The wearable sensors continuously monitor head impacts and the processing device estimates brain injury in real-time, enabling early warning and intervention before long-term damage accumulates.
2Measurement precision
If extensive imaging such as MRI is used to estimate brain damage, then the measurement precision is high, but the device complexity and cost increase
Solution Approach 1:
The system extracts only the essential information needed for brain injury estimation from force measurements. Instead of using complex imaging systems, the processing device calculates brain damage estimates by analyzing force magnitude, direction, and duration data from simple wearable sensors, extracting the critical parameters needed for injury assessment without requiring full imaging.
Solution Approach 2:
The system creates a computational model that replicates the complex biomechanical relationships between head impacts and brain injury. The processing device uses algorithms to simulate and predict brain damage based on measured forces, creating a virtual representation of brain injury risk without requiring physical imaging procedures.
3Speed
If wearable sensors are used to measure forces in real-time, then the detection speed is high, but the measurement precision may be reduced compared to laboratory equipment
Solution Approach 1:
The system compensates for sensor limitations by analyzing multiple parameters simultaneously - force magnitude, direction, duration, and rate of change. The processing device evaluates combinations of these parameters to estimate brain injury, using multi-parameter analysis to overcome the reduced precision of individual wearable sensor measurements.
Data Source
AI summary
A system is configured for receiving force data including at least one value indicating the amount of the force applied to the portion of the user and at least one value indicating a direction of the force applied to the portion of the user; obtaining mapping data specifying at least one relation between values of force applied to the portion of the user and changes in a functional responsiveness, functional and/or structural integrity, or both the functional responsiveness and the functional and/or structural integrity of the brain at one or more locations in the brain; estimating, based on the mapping data and the force data, an amount of force loading at one or more particular locations in the brain; and generating, based on the estimating, output data representing an amount of the damage to the brain at the one or more particular locations in the brain.