Knee-Mounted Accelerometer for ACL Injury Prediction
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Solution Overview
Problem
Current methods fail to effectively predict and prevent anterior cruciate ligament (ACL) injuries in non-contact situations, which are common among athletes engaging in dynamic movements such as jumping, landing, and sudden deceleration, due to the lack of understanding of the forces generated on the knee during these activities.
Innovation Solution
A computer-implemented system that integrates a microcontroller with an accelerometer strapped to the knee, measuring linear acceleration during dynamic movements and transmitting data to a predictive AI model for risk assessment, utilizing cloud computing and machine learning algorithms to provide injury prediction and personalized exercise recommendations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional methods are used to monitor knee health, then device complexity is low, but measurement precision of forces generated during dynamic movements is insufficient
Solution Approach 1:
The patent combines multiple sensing capabilities (accelerometry, gyroscopy, force sensing) into an integrated knee-mounted apparatus. This merging of sensors allows comprehensive measurement of forces during dynamic movements while managing device complexity through integration rather than separate components.
Solution Approach 2:
The patent introduces computational models and algorithms as intermediaries that process raw sensor data to derive meaningful force measurements. These computational intermediaries transform complex sensor signals into actionable injury risk assessments without requiring direct mechanical measurement of all forces.
2Loss of information
If comprehensive sensor data collection is implemented, then information completeness for injury prediction is improved, but loss of time for data processing increases
Solution Approach 1:
The patent pre-processes and filters sensor data during collection, preparing it in advance for analysis. By performing preliminary data conditioning, cleaning, and feature extraction at the point of collection, the system reduces the computational burden during injury prediction, thus minimizing data processing time while maintaining information completeness.
Solution Approach 2:
The system implements continuous feedback loops where processed data informs real-time adjustments in monitoring parameters. This feedback mechanism allows the system to prioritize critical data streams and adjust processing intensity based on detected movement patterns, optimizing both information capture and processing efficiency.
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 predicts the risk of ACL injuries by analyzing forces generated during dynamic movements, providing athletes and trainers with actionable insights to prevent injuries, thereby reducing the likelihood of non-contact ACL tears.
Implementation Method 1
Measure linear acceleration along three dimensions using the accelerometer
Data Source
AI summary
Described herein is a computer implemented method and system of predicting risk of injury to an individual. Integrate a microcontroller with one or more sensors including but not limited to an accelerometer, gyroscope, skin pH sensor, temperature and humidity sensor, and strap it to the individual's body part. Instruct the individual to perform one dynamic movement. Measure linear acceleration along three dimensions using the accelerometer. Collect data from the microcontroller and transmit the data to a predictive AI model. Receive a response from the predictive AI model on the risk of injury.