In orthopedics, X-rays and computed tomography (CT) scans play pivotal roles in diagnosing and treating bone pathologies. Machine bulkiness and the emission of ionizing radiation remain the main problems associated with these techniques. The accessibility and low risks related to ultrasound handling make it a popular 2D imaging method. Indeed, 3D ultrasound assembles 2D slices into a 3D volume. This study aimed to implement a probe-tracking method for 6 DoF 3D ultrasound. The proposed method involves a dodecahedron with ArUco markers attached, enabling computer vision tracking of the ultrasound probe’s position and orientation. The algorithm focuses on the data acquisition phase but covers the basic reconstruction required for data generation and analysis. In the best case, the analysis revealed an average error norm of 2.858 mm with a standard deviation norm of 5.534 mm compared to an infrared optical tracking system used as a reference. This study demonstrates the feasibility of performing volumetric imaging without ionizing radiation or bulky systems. This marker-based approach shows promise for enhancing orthopedic imaging, providing a more accessible imaging modality for helping clinicians to diagnose pathologies regarding complex joints, such as the shoulder, replacing standard infrared tracking systems known to suffer from marker occlusion problems.

Purpose: This study evaluated whether movement smoothness metrics can effectively distinguish between pathological and healthy upper limbs in patients with rotator cuff tears (RCT), and aimed to identify the most reliable metrics across time and frequency domains. It also investigated the task dependence of movement smoothness across different planes of arm elevation. Methods: Thirty‐three patients with unilateral RCT performed bilateral synchronous arm elevation tasks in sagittal, scapular, and frontal planes. Movement smoothness was quantified using metrics such as log dimensionless squared jerk (LDSJ), mean arrest period ratio (MAPR) and spectral arc length (SPARC, SPAL), obtained using a 3D Optoelectronic motion capture system. The coefficient of variation (CoV) was used to assess the inter‐subject variability. Differences between limbs were tested with the Mann–Whitney U test; task dependency was evaluated with the Wilcoxon signed‐rank test. Results: Pathological limbs consistently showed lower smoothness than healthy ones across all planes, with SPAL, SPARC, DSJ and LDSJ significantly differentiating the two sides ( p < 0.05). SPARC and MAPR exhibited the lowest inter‐subject variability, supporting their validity. Smoothness was also task‐dependent, with frontal plane movements being significantly smoother than those in the sagittal or scapular planes ( p < 0.001). Conclusion: Smoothness metrics can effectively differentiate pathological from healthy limb movements in patients with RCT, with variability across movement planes. Among the tested metrics, SPARC emerged as the most robust and consistent measure, supporting its use as a biomechanical biomarker for functional assessment and clinical decision‐making. Level of Evidence: Level II.