Wearable Concussion Detection Circuit Using Accelerometer Thresholds
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Solution Overview
Problem
Concussions often go undiagnosed and unreported, leading to increased risk of permanent brain damage and further injuries, as athletes may not recognize or report symptoms due to fear of being removed from play, resulting in approximately 39% of subsequent impacts causing severe brain damage.
Innovation Solution
A wearable concussion detecting circuit with an accelerometer, processing unit, and communication module that compares impact acceleration data to predetermined threshold values to detect concussions and transmit alerts and data to a server, allowing for timely intervention and reporting.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If concussion detection systems are implemented, then concussion detection accuracy is improved, but device complexity increases
Solution Approach 1:
The concussion detection system is segmented into distinct functional modules: accelerometer sensors for data collection, processing units for analysis, and communication modules for reporting. This modular segmentation enables accurate concussion detection while managing system complexity through organized, independent components that can be developed and maintained separately.
Solution Approach 2:
The detection system is designed with multi-functionality to handle various concussion scenarios and reporting requirements through a unified platform. The system can detect different types of impacts, adapt to various user profiles (athletes, military personnel), and communicate through multiple channels, reducing the need for multiple specialized systems.
2Loss of time
If real-time concussion detection and alert transmission is implemented, then response time is improved, but energy consumption increases
Solution Approach 1:
The system employs periodic action by transmitting alerts and data at specific intervals or triggered events rather than continuous transmission. The accelerometer collects data continuously but only initiates communication when concussion thresholds are exceeded, reducing energy consumption while maintaining rapid response capability when needed.
Solution Approach 2:
The system includes automatic alert transmission and self-reporting capabilities that operate autonomously without requiring constant user intervention or manual reporting. The processing unit automatically analyzes sensor data, determines concussion events, and triggers communications, reducing the energy burden of manual operations while maintaining rapid response.
3Measurement precision
If comprehensive sensor data collection is implemented, then measurement accuracy is improved, but data processing complexity increases
Solution Approach 1:
The system extracts and focuses on the most critical data elements for concussion detection from the comprehensive sensor data. The processing unit identifies and isolates key parameters such as impact acceleration, direction, and duration, separating essential concussion-related information from other sensor data, thereby improving detection accuracy while reducing processing complexity.
Solution Approach 2:
The system transforms raw sensor data into meaningful concussion detection parameters by applying specific processing algorithms and threshold comparisons. The processing unit converts complex multi-axis accelerometer data into simplified impact metrics that can be directly compared against concussion thresholds, maintaining measurement precision while reducing data processing complexity through parameter transformation.
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
The system effectively detects concussions by transmitting alerts and data to coaches and athletes, reducing the risk of further injuries and promoting timely medical attention, thereby reducing the incidence of permanent brain damage.
Implementation Method 1
The sensor data may include acceleration data measured by an accelerometer
Data Source
AI summary
Disclosed systems and methods relate to concussion detection and reporting. In an example, a method involves receiving sensor data from an accelerometer. The method further involves determining an impact acceleration from the sensor data and comparing the impact acceleration to a threshold value. The threshold value may be adjustable based on a user's age. When the impact acceleration is less than the threshold value, the method involves transmitting the sensor data to a server. When the impact acceleration equals or exceeds the threshold value, the method involves transmitting an alert and transmitting the sensor data to a server.