Biomechanics-aware Multi-view Markerless Motion Capture of Dexterous Hand Movements

Markerless motion capture (MMC) techniques have been widely beneficial in biomechanical analysis of human movement; however, application to complex motions of the hand lags other musculoskeletal systems. The primary goal of this study was to evaluate the performance of a biomechanical reconstruction method that implements a gradient-based optimization approach with a biomechanical model in the loop for tracking dexterous, unconstrained hand movements using MMC. Using a custom, 8-camera setup, we acquired 121 video recordings from 6 participants performing 11 different tasks that spanned 6 hand postures, 5 object manipulation tasks, and involved motion of the proximal upper limb joints. Performance of the proposed MMC pipeline was directly compared to a more commonly adopted two-stage reconstruction method that first triangulates 2D keypoints from computer vision pose estimation algorithms to 3D and then enforces biomechanical constraints by solving a constrained inverse kinematics problem. Relative performance was assessed qualitatively by visual inspection and quantitatively using a computer vision metric. Our method generated solutions for all 121 video recordings; the two-stage method did not converge for 15% of the recordings. Across the remaining videos, our method produced more biomechanically plausible hand kinematics than the two-stage method and was more robust to occlusion effects during tasks that involved objects. The relative robustness of the end-to-end method suggests that it is more effective in utilizing the available 2D digital keypoint information. Automatic and biomechanically meaningful tracking of hand kinematics during dexterous movements has the potential to support clinical evaluation, rehabilitation monitoring, and studies of human motor control.

Touchless Intraoperative Image Access System Based on Vision-Based Hand Tracking

Touchless interaction with medical images is becoming increasingly important in the surgical field, where sterility and continuity of the operational workflow are essential requirements. This work presents a vision-based system for intraoperative navigation of medical images through hand gestures acquired using a single RGB camera. Unlike many existing solutions, the system does not require additional hardware or user-specific training. Hand tracking is performed in real time using MediaPipe Hands, which provides a 2.5D estimation of hand landmarks. Simple and intuitive gestures are then mapped into translation, rotation, and zoom commands, enabling continuous and natural interaction with the image viewer. The system architecture is independent from the visualization software and, for implementation simplicity, in this study it was integrated with PyVista. Performance was evaluated through frame-level logging and quantitative analysis of latency, stability, and interaction robustness metrics. Experimental results highlight real-time behavior, with reduced latencies and stable control, in line with the requirements of fluid interaction. The system demonstrates the feasibility of a low-cost touchless solution for intraoperative access to medical images, laying the groundwork for future clinical evaluations.