# AI-Based Electromyographic Analysis of Single-Leg Landing for Injury Risk Prediction in Taekwondo Athletes

## TL;DR

This study uses AI to analyze muscle activity during Taekwondo landings to predict injury risk, showing promising results for identifying athletes with poor landing mechanics.

## Contribution

The novel use of AI models to predict injury risk in Taekwondo athletes based on EMG data during single-leg landings.

## Key findings

- Random Forest Classifier achieved 83.65% accuracy in distinguishing between regular and non-exercise groups.
- Ridge Regression showed strong performance with an R2 score of 0.9974 in predicting muscle activation changes.
- The AI model demonstrated potential for assessing landing stability and injury risk in Taekwondo athletes.

## Abstract

Background/Objectives: Improper landing mechanics in Taekwondo can lead to non-contact injuries such as ankle sprains and knee ligament tears, highlighting the necessity for objective methods to evaluate landing stability and injury risk. Electromyography (EMG) enables the examination of muscle activation patterns; however, conventional analyses based on simple averages have limited predictive value. Methods: This study analyzed EMG signals recorded during single-leg landings (45 cm height) in 30 elite male Taekwondo athletes. Participants were divided into regular exercise groups (REG, n = 15) and non-exercise groups (NEG, n = 15). Signals were segmented into two phases. Eight features were extracted per muscle per phase. Classification models (Random Forest, XGBoost, Logistic Regression, Voting Classifier) were used to classify between groups, while regression models (Ridge, Random Forest, XGBoost) predicted continuous muscle activation changes as injury risk indicators. Results: The Random Forest Classifier achieved an accuracy of 0.8365 and an F1-score of 0.8547. For regression, Ridge Regression indicated high performance (R2 = 0.9974, MAE = 0.2620, RMSE = 0.4284, 5-fold CV MAE: 0.2459 ± 0.0270), demonstrating strong linear correlations between EMG features and outcomes. Conclusions: The AI-enabled EMG analysis can be used as an objective measure of the study of the individual landing stability and risk of injury in Taekwondo athletes, but its clinical application has to be validated in the future by biomechanical injury indicators and prospective cohort studies.