Evaluating Assistive Technology Outcomes in Boccia Athletes with Disabilities Using AI-Based Kinematic Analysis

This study explores how artificial intelligence (AI) can support the evaluation of assistive technology outcomes in adaptive sports, focusing on elite boccia athletes with disabilities. Using a multi-stage motion analysis framework, we integrated OpenPose, ViTPose, and Lifting to estimate seated joint kinematics with greater precision. Match footage from 12 athletes at the 2018 Asia-Pacific Boccia Open was analyzed across five biomechanical phases: preparation, acceleration, peak, release, and follow-through. AI-enhanced 2D and 3D pose estimation methods were applied to assess throwing strategies and motor variability. ViTPose outperformed OpenPose in joint detection accuracy (F1-score: 85% vs. 79.5%), while Lifting improved 3D estimation by reducing joint position error by 16%. Principal Component Analysis revealed greater movement consistency in overhand throws compared to underhand techniques. The proposed pipeline provides an interpretable and scalable method for measuring performance, motor control, and strategy-specific movement outcomes in boccia, offering practical applications for evidence-based coaching, athlete classification, and the design of inclusive assistive sport technologies.