Evaluating the Influence of Physical Activity on Gait Aging Using Multilevel Machine Learning

Gait is a fundamental and functional activity among older adults; however, aging leads to anatomical and functional gait deterioration. This decline can be mitigated through regular physical activity. Biomechanical analysis—using electromyography, kinematics, and force measurements—offers one of the most objective methods for assessing gait. In this study, we propose a novel framework based on machine learning techniques to identify biomarkers that more precisely distinguish the gait patterns of young adults from those of physically active older adults. Gait analysis included kinematic, kinetic, and surface electromyography (sEMG) data, all recorded under controlled walking speed conditions. The extracted features comprised joint kinematics of the pelvis, hip, knee, and ankle; ground reaction forces (GRF); and muscle activation signals from six muscles in the dominant lower limb. Among the evaluated models, the proposed Multilevel XGBoost approach achieved the highest performance, improving classification accuracy by 13.1%, reaching 80.24%. Key biomarkers identified included pelvic tilt adjustments, reduced ankle range of motion, and altered muscle activation patterns—changes associated with stability-related adaptations in the aging gait. These findings highlight that biomechanical changes are detectable even among older adults who maintain regular physical activity. Future research will aim to integrate deep learning and fuzzy logic techniques to enhance feature extraction and improve the analysis of gait variability.