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.
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arXiv:2609.35726v2 Announce Type: replace Abstract: Automated quality assessment of rehabilitation exercises relies heavily on accurate human pose estimation from video data. Although numerous RGB-ba...
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.
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...
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:2609.18406v1 Announce Type: new Abstract: Recovering 3D human body motion from video is important for applications such as rehabilitation assessment and sports performance evaluation. For prost...
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.