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This study explored the potential of gait analysis as a tool for assessingpost-injury complications, e.g., infection, malunion, or hardware irritation,in patients with lower extremity fractures. The research focused on theproficiency of supervised machine learning models predicting complicationsusing consecutive gait datasets. We identified patients with lower extremityfractures at an academic center. Patients underwent gait analysis with achest-mounted IMU device. Using software, raw gait data was preprocessed,emphasizing 12 essential gait variables. Machine learning models includingXGBoost, Logistic Regression, SVM, LightGBM, and Random Forest were trained,tested, and evaluated. Attention was given to class imbalance, addressed usingSMOTE. We introduced a methodology to compute the Rate of Change (ROC) for gaitvariables, independent of the time difference between gait analyses. XGBoostwas the optimal model both before and after applying SMOTE. Prior to SMOTE, themodel achieved an average test AUC of 0.90 (95% CI: [0.79, 1.00]) and testaccuracy of 86% (95% CI: [75%, 97%]). Feature importance analysis attributedimportance to the duration between injury and gait analysis. Data patternsshowed early physiological compensations, followed by stabilization phases,emphasizing prompt gait analysis. This study underscores the potential ofmachine learning, particularly XGBoost, in gait analysis for orthopedic care.Predicting post-injury complications, early gait assessment becomes vital,revealing intervention points. The findings support a shift in orthopedicstowards a data-informed approach, enhancing patient outcomes.

Machine Learning Based Analytics for the Significance of Gait Analysis in Monitoring and Managing Lower Extremity Injuries