Dual-Camera System for AI Markerless 3D Lower Limb Motion Analysis

Motion analysis plays a fundamental role in numerous fields, from sports science to rehabilitation, allowing the study and evaluation of human movement. Traditional marker-based systems, while highly accurate, present limitations such as high costs, complex setup, and restrictions in natural movement. To overcome these issues, markerless motion capture methods have gained increasing attention, exploiting advances in computer vision and machine learning.This work was born by the experimental activity conducted during the ActivE3 project at the Human Performance Laboratory at the Lecco Campus of the Politecnico di Milano. During the project, aimed to explore new methodologies for rehabilitation and movement analysis and to promote inclusiveness and accessibility in physical activity, the Virtual Reality Nirvana (BTS Bioengineering SpA, Garbagnate Milanese, Milan, Italy) system was employed. Although the system provided highly engaging and beneficial for enhancing movement participation, it lacked the capability to quantitatively measure joint kinematics. This limitation highlighted the necessity for a reliable, markerless motion tracking solution, to bridge the gap between interactive rehabilitation technologies and precise motion tracking, ensuring a quantitative evaluation. The study focuses on the development of a markerless motion analysis system designed to be accessible and reliable for clinical and sports applications. The proposed system employs two cameras to record movement, the MediaPipe framework to extract body keypoints, and MATLAB for the computation of knee flexion-extension angles and data processing.The system was validated through experimental trials involving human subjects performing standardized motion tasks. The computed joint angles were compared against measurements obtained using an IMU-based motion capture system, which is considered with high level of accuracy in the field. Quantitative evaluation metrics such as RMSE, ICC, Spearman’s rank correlation coefficient and bias were used to assess system performance. The results demonstrate that the proposed markerless system provides accurate joint angle estimations, supporting its potential application in real-world scenarios where traditional motion capture is impractical.