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: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.
By Manex Atxa, Bruno Simoes, Julen Balzategui
arXiv:2506. 17332v2 Announce Type: replace-cross Abstract: By 2050, people aged 65 and over are projected to make up 16% of the global population.
By Haitian Wang, Yiren Wang, Xinyu Wang, Yumeng Miao, Yuliang Zhang, Yu Zhang, Atif Mansoor
arXiv:2606. 00345v1 Announce Type: new Abstract: Wearable and mobile sensing technologies enable continuous monitoring of human behavior and health in real-world settings.
By Flavio Di Martino, Mattia G. Campana, Marcello Magno, Lorenza Pratali, Franca Delmastro
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.
By Ayda Eghbalian, Kevin Desai
arXiv:2607. 06954v1 Announce Type: new Abstract: Wearable and mobile sensing technologies have demonstrated strong potential for health inference; however, most sensor models are designed for specific disease types, limiting their transferability across different health risks.
By Zhenghuang Wu, Yuyao Zhu, Songlin Xu