AI-BASED EMG ANALYSIS FOR INJURY RISK PREDICTION
Revolutionizing Taekwondo Injury Prevention with AI-Powered Electromyography
This cutting-edge research applies artificial intelligence to electromyography (EMG) data from Taekwondo athletes performing single-leg landings. By analyzing muscle activation patterns, our AI models can objectively assess landing stability and predict injury risk, offering a proactive approach to athlete safety and performance optimization.
Executive Impact: Key Metrics & AI Advantages
Our AI-driven analysis provides unprecedented precision in understanding neuromuscular control. These validated metrics demonstrate the power of machine learning to identify complex patterns indicative of injury risk and performance.
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Classification Accuracy (Random Forest)
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F1-Score (Random Forest)
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Regression R2 Score (Ridge Regression)
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Lowest MAE (Ridge Regression)
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
Comprehensive Data-to-Insight Pipeline
Our study utilized a dual strategy for robust predictive modeling. EMG signals from nine lower-limb muscles were collected during single-leg landings, simulating Taekwondo actions. These signals underwent extensive feature engineering, transforming raw data into meaningful metrics like activation ratios and change percentages across two distinct landing phases. Data augmentation, including SMOTE, was applied to address class imbalance and enhance model reliability. This prepared the dataset for both classification and regression tasks, ensuring a comprehensive analysis of neuromuscular control.
Enterprise Process Flow
EMG Data Acquisition
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Feature Engineering & Optimization
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Model Training (Classification & Regression)
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Performance Evaluation & Interpretation
Superior Classification for Injury Risk Identification
The classification task focused on distinguishing between stable and unstable landing patterns, a critical indicator of injury risk. We rigorously evaluated several machine learning algorithms, including Logistic Regression, Random Forest, XGBoost, and a Voting Classifier. The Random Forest Classifier emerged as the top performer, achieving an 83.65% accuracy and an F1-score of 0.8547. This demonstrates its superior ability to capture complex, non-linear muscle coordination patterns essential for identifying subtle movement variations linked to injury susceptibility.
83.65%
Peak Classification Accuracy (Random Forest)
Precise Muscle Activation Prediction
For predicting continuous muscle activation changes, an index of injury risk, regression models were employed. The Ridge Regression (RR) model delivered an exceptionally high R2 score of 0.9999 and a remarkably low Mean Absolute Error (MAE) of 0.2620. This near-perfect fit highlights the strong linearity between our engineered EMG features (P1/P2 values, activation ratios) and the target variable. While more complex models like Random Forest and XGBoost also performed well, the simplicity and stability of Ridge Regression proved highly effective for this specific task.
| Model | R2 Score | MAE | Key Finding |
|---|---|---|---|
| Ridge Regression | 0.9999 | 0.2620 | Almost perfect linear fit, highly stable. |
| Random Forest Regressor | 0.9982 | 0.6260 | High R2, but larger individual prediction errors. |
| XGB Regression | 0.9997 | 0.4302 | Strong performance, but not superior to Ridge for this linear relationship. |
Bridging the Gap: From Statistical Precision to Clinical Utility
Problem: While AI models achieved high statistical accuracy in predicting neuromuscular changes, the direct correlation with actual injury incidence and validated biomechanical markers remains unvalidated. This limits immediate clinical application as a predictive injury tool.
Solution: Future research must integrate biomechanical injury risk metrics (e.g., knee valgus angles, vertical loading rates) and adopt prospective, longitudinal study designs to correlate EMG-derived predictions with observed injury rates. Only through such validation can AI-based EMG truly serve as a proactive diagnostic tool for injury prevention in Taekwondo.
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Estimated Annual Savings
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Annual Hours Reclaimed
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Your AI Implementation Roadmap
Our proven four-phase approach ensures a seamless and effective integration of AI into your sports science or healthcare operations.
Phase 1: Data Audit & Strategy Alignment
Evaluate existing data infrastructure, define AI objectives, and align with stakeholder goals. Establish key performance indicators (KPIs) for success.
Phase 2: AI Model Development & Integration
Custom build and train AI models using your data, integrate with existing systems for real-time insights. Focus on robust feature engineering and model validation.
Phase 3: Pilot Program & Validation
Deploy AI solutions in a controlled environment, rigorously validate performance, and gather user feedback. Refine models based on real-world application.
Phase 4: Full-Scale Deployment & Continuous Optimization
Roll out AI across your organization, establish monitoring protocols, and iterate for peak performance and long-term impact on injury prevention.
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Don't let preventable injuries hinder performance. Partner with us to unlock the full potential of AI for predictive injury risk assessment and enhance athlete longevity.
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