HandSplatter: Automated Digital Goniometry from Neural Rendering
2026-08-10 • Computer Vision and Pattern Recognition
Computer Vision and Pattern Recognition
AI summaryⓘ
The authors developed a new computer method to measure how well fingers and hand joints move, which is important for treating hand problems. Traditional tools like goniometers can be slow and sometimes give inconsistent results. Their approach uses advanced 3D modeling and a special algorithm to improve the accuracy of finger motion measurements. This method aims to be more reliable and easier to use than existing manual and digital options.
range of motiongoniometerhand joint pose estimationneural rendering2D feature extractionview synthesisdensity hill climbing algorithmmusculoskeletal disabilityfunctional assessment
Authors
Emmett Chen, Neal Chen, Xiang Li, Quanzheng Li, Siyeop Yoon
Abstract
Hand and finger disorders are leading contributors to musculoskeletal disability, creating a clinical need for precise methods to quantify joint motion. Range of motion (ROM) serves as the metric for diagnosis, rehabilitation monitoring, and evaluating surgical outcomes. Currently, the goniometer is the standard tool for assessing finger flexion and extension. However, manual goniometry is labor-intensive and suffers from inconsistent inter-rater reliability due to variations in examiner technique. While digital alternatives exist, current software-based approaches often lack the necessary accuracy for clinical usage. To address these limitations, we present a novel pipeline for 3-D hand joint location and pose estimation using neural rendering. Unlike previous methods, our approach combines 2-D feature extraction with view synthesis to significantly improve accuracy and clinical viability. Furthermore, we introduce a discrete density hill climbing algorithm that facilitates the meaningful correction of projected landmarks in 3-D space. This system overcomes the inefficiencies of manual measurement and the inaccuracies of existing software, providing a robust tool for objective functional assessment.