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.
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:2606. 28104v1 Announce Type: cross Abstract: Vision-based assessment can provide convenient and cost-effective evaluation in Traditional Chinese Medicine (TCM) rehabilitation training, where action quality assessment (AQA) from computer vision offers a promising solution.
This scoping review examined 117 studies on video-based markerless motion capture, most published from 2024 onward and focused on healthy adults walking in laboratories. The studies identified five main pipeline architectures, but most reported only raw joint angles without biomechanical refinement, achieving sagittal lower‑limb agreement of about 5–6°, which falls short of clinical acceptability. Validation of out‑of‑plane kinematics, kinetics, and performance in older or pathological populations was rare, and emerging computer‑vision techniques such as foundation‑model mesh recovery and differentiable inverse kinematics were largely absent from validated work.
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:2608.16081v2 Announce Type: replace Abstract: Open-weight and frontier vision-language models (VLMs) perform well on general image understanding, but their ability to interpret fine-grained han...
Estimating physical pressure from vision is essential for understanding contact-rich hand-object interaction. However, prior vision-based pressure estimation methods are largely limited to planar surfaces and single image input, making them difficult to apply to dynamic hand-object interaction with diverse objects.
arXiv:2606. 04708v1 Announce Type: cross Abstract: Universal Manipulation Interface (UMI) enables scalable real-world robot data collection without hardware-specific teleoperation, yet leveraging UMI data to train large-scale Vision-Language-Action (VLA) models remains fundamentally challenging.
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.
Universal Manipulation Interface (UMI) enables scalable real-world robot data collection without hardware-specific teleoperation, yet leveraging UMI data to train large-scale Vision-Language-Action (VLA) models remains fundamentally challenging. We identify two critical mismatches: wrist-mounted fisheye views, with severe radial distortion and local gripper-centric perspectives, are out-of-distribution for pretrained VLMs; and human-collected trajectories frequently violate kinematic limits, incur collisions, or exceed controller bandwidth, teaching VLA policies physically infeasible actions.
arXiv:2609.08038v2 Announce Type: cross Abstract: Smart healthcare monitoring systems require precise action recognition to ensure well-being and timely intervention in critical situations such as fa...
TouchSight is a monocular egocentric vision framework that predicts dense full-hand contact forces from video. It uses 500 hours of pressure‑glove recordings and hand‑object interaction data, and introduces TwinTouch‑20H, a dataset of 20 hours of paired visual data where generative models render gloved recordings as bare‑hand observations while preserving tactile labels. The system outperforms prior methods on OakInk2, generalizes qualitatively to natural bare‑hand egocentric videos from unseen datasets, and improves consistently as glove supervision scales.
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.