A Novel Technical Framework for Enhancing Biomechanical Data Acquisition in Clinical Orthopedics

Abstract

In the realm of clinical orthopedics, accurate biomechanical data acquisition is critical for diagnosis and intervention. Traditional methodologies often fall short in precision, leading to compromised patient outcomes. This study presents a novel technical framework leveraging advanced real-time motion capture systems integrated with machine learning algorithms for data enhancement. We employed a dual-phase empirical method: Phase I involved the calibration of motion capture devices using Vicon Nexus 2.13, while Phase II integrated data refinement through Python-based machine learning libraries (scikit-learn v0.24.2). Quantitative findings demonstrated a 25% reduction in data acquisition error and a significant improvement in the reproducibility of biomechanical assessments, as evidenced by lower coefficient of variation (CV = 5.2%) compared to conventional techniques. This work not only delivers an innovative approach to biomechanical data acquisition but also paves the way for future research into automated clinical diagnostic systems.