Ensemble Approach for Enhanced Classification of Timed Up and Go Test Movements
Yudhi Ardiyanto, Kusworo Adi, Kurnianingsih Kurnianingsih · JOIV International Journal on Informatics Visualization · 2025
This study aims to evaluate the classification accuracy of a video-based system for Timed Up and Go (TUG) subtasks using human pose estimation through MediaPipe. Six participants were included in the validity study, all participating in the reliability study, performing various TUG subtasks. The research methodology involved acquiring video data that captured the participants' movements during the TUG activity. This video data was processed using the MediaPipe package to extract key points from each frame, resulting in a 2D skeletal representation. The dataset was imported in CSV format to train multiple machine learning algorithms. The dataset was partitioned into training data (70%) and test data (30%), and several machine learning models, including Stacking Ensemble, Hist Gradient Boosting, XGBoost, CATBoost, Random Forest, and Gradient Boosting, were evaluated for their effectiveness in classifying TUG subtasks. The evaluation was conducted by comparing the classification accuracy of each model with the posture detection outcomes and overall performance metrics. The results indicated that the Stacking Ensemble method achieved the highest overall accuracy (96.90%), outperforming models such as Hist Gradient Boosting (96.48%), XGBoost (95.63%), CATBoost (96.06%), Random Forest (95.92%), and Gradient Boosting (95.21%). Each classifier was evaluated across sub-activities, and the results consistently demonstrated the superior performance of the Stacking Ensemble. These findings suggest that the video-based system, when combined with advanced machine learning techniques and human pose estimation, is a reliable and accurate tool for measuring and classifying subtask movements in TUG among older adults.