The field of human movement analysis and medical imaging is rapidly evolving, with a focus on developing innovative methods for understanding and interpreting complex data. Recent developments have centered around improving the accuracy and efficiency of motion tracking and correction techniques, with applications in fields such as action recognition, cardiovascular magnetic resonance, and jaw motion tracking. Notably, deep learning-based approaches have shown significant promise in enhancing the robustness and scalability of motion correction pipelines. Additionally, advances in 3D human pose estimation have enabled more accurate assessments of human movement in daily living activities, with potential applications in telemedicine, sports science, and rehabilitation. Noteworthy papers include:

• ProDA, which proposes a novel framework for disentangling specified actions from complex scenes, leveraging Spatio-temporal Scene Graphs and Dynamic Prompt Module.
• AssemblyHands-X, which introduces a markerless 3D hand-body benchmark for bimanual activities, demonstrating the importance of modeling interdependent hand-body dynamics for action recognition.
• A deep learning-based motion correction pipeline for quantitative stress perfusion cardiovascular magnetic resonance, which enables fast and robust motion correction, improving accuracy and reducing processing time.
• An open-source optical measurement system for tracking jaw motions, which provides a precise, non-invasive, and biocompatible solution for diagnosing musculoskeletal and neuromuscular diseases.
• A preclinical benchmark comparing monocular video-based 3D human pose estimation models with inertial measurement units, highlighting the potential of deep learning-based approaches for kinematic assessment in daily living activities.