arXiv:2511. 20615v2 Announce Type: replace-cross Abstract: This study aimed to explore the application of deep neural networks for whole-body human posture prediction during dynamic load-reaching activities.
arXiv:2606. 12988v1 Announce Type: cross Abstract: This paper introduces a new methodology for real-time prediction of ergonomic and non-ergonomic human poses using volumetric video data in three dimensions.
arXiv:2608. 12145v1 Announce Type: cross Abstract: Autonomous rehabilitation systems must not only recognize human motion but also provide structured feedback to support users without continuous therapist supervision.
arXiv:2507. 21018v2 Announce Type: replace-cross Abstract: Automated assessment of human motion plays a vital role in rehabilitation, enabling objective evaluation of patient performance and progress.
arXiv:2607. 13216v1 Announce Type: cross Abstract: Humans recognize movements effortlessly, even from noisy and complex visual input.
Radar-based human pose estimation has focused on improving learning algorithms while representing the body as unconstrained keypoint coordinates. We address the underexplored dimension of anatomical fidelity by integrating a full-body skeletal model into a differentiable, end-to-end trainable radar-based pose estimation framework, in which the pose network is supervised through forward kinematics while subject-specific geometry is fitted beforehand.
arXiv:2607. 08725v1 Announce Type: cross Abstract: Recent progress in 3D human pose estimation has made markerless recovery of skeletal motion increasingly accurate and scalable.
The study presents a vision‑language model pipeline that estimates dynamic, triaxial, bilateral external hand forces during manual material handling tasks using only RGB video and known box mass. By combining text‑guided ROI localization, pretrained vision‑transformer features, and transformer‑based temporal regression, the model achieved root mean square errors of about 4.7–5.6 N for horizontal and mediolateral forces and 10.6–11.0 N for vertical forces across various camera setups. The approach demonstrated that including the handled object as a second ROI and using multi‑camera capture improved peak‑force estimation, showing the feasibility of sensor‑free force estimation for occupational exposure assessment.
arXiv:2608.29928v1 Announce Type: new Abstract: State-of-the-art monocular body recovery methods predict mesh vertices and angles on the corresponding kinematic tree, but their outputs lack biomechan...
External hand forces are important inputs to biomechanical analyses of occupational physical exposure and injury risk, yet continuous force measurements during manual material handling (MMH) typically...
arXiv:2401.05018v3 Announce Type: replace Abstract: Human motion prediction is a crucial capability for advanced robotic systems that interact with humans. In facilities with dynamic human-robot coll...
arXiv:2608. 02408v1 Announce Type: new Abstract: Accurate gait analysis in Parkinson's disease (PD) typically relies on laboratory-based systems to capture biomechanical data, such as ground reaction forces (GRFs).
arXiv:2608. 19480v1 Announce Type: new Abstract: Human pose estimation has advanced significantly due to the development of deep learning models, increased data availability, and improved computing resources.