Hugging Face Trending Papers

Direct Clinical Joint Angle Extraction from Parametric Body Model Rotation Matrices

Quantitative joint angles are rarely available in routine care because the tools are slow, costly, or confined to a laboratory. We show that clinical joint angles can be read directly from the per-segment rotation matrices a parametric body model already produces, with no inverse-kinematics or musculoskeletal-model fitting step.

Hugging Face Trending Papers
Aug 4

Learning Biomechanically Plausible Human Motion from Sparse Radar Point Clouds

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 AI
Jun 2

Quantitative Movement Testing: Measuring Patient Movements from a Single Smartphone Video

arXiv:2606. 02301v1 Announce Type: cross Abstract: Chronic pain diminishes quality of life by decreasing functional ability, yet objectively measuring this functional impact remains challenging in real-world settings.

By Pranav Mahajan, Amanda Wall, Eleonora Maria Camerone, Julie Stebbins, Eoin Kelleher, Shuangyi Tong, Annina Schmid, Katja Wiech, Anushka Irani, Ben Seymour
arXiv AI
Jul 1

Quantitative Movement Testing: Measuring Chronic Pain Patient Movements from a Single Smartphone Video

arXiv:2606. 02301v2 Announce Type: replace-cross Abstract: Chronic pain diminishes quality of life by decreasing functional ability, yet objectively measuring this functional impact remains challenging in real-world settings.

By Pranav Mahajan, Amanda Wall, Eleonora Maria Camerone, Julie Stebbins, Eoin Kelleher, Shuangyi Tong, Annina Schmid, Katja Wiech, Anushka Irani, Ben Seymour
arXiv AI
Jun 6

Tamaththul3D: High-Fidelity 3D Saudi Sign Language Avatars from Monocular Video

arXiv:2605. 05367v2 Announce Type: replace-cross Abstract: Existing 3D sign language avatar reconstruction methods are developed and evaluated exclusively on Western sign languages, and no 3D parametric annotations exist for any Arabic Sign Language dataset, a gap that blocks the development of avatar-based accessibility applications for the Arab Deaf community.

By Eyad Alghamdi, Sattam Altuuaim, Obay Ghulam, Abdulrahman Qutah, Yousef Basoodan
arXiv Machine Learning
Jun 29

Cross-view Multimodal Vision-Based Assessment Framework for Traditional Chinese Medicine Rehabilitation Training

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.

By Francis Xiatian Zhang, Hao Yao, Shengxuan Chen, Hong Zhu, Hongxiao Jia, Sisi Zheng, Hubert P. H. Shum
arXiv Machine Learning
Jul 2

IRIS: A Real-World Benchmark for Inverse Recovery and Identification of Physical Dynamic Systems from Monocular Video

arXiv:2603. 16432v3 Announce Type: replace-cross Abstract: Unsupervised physical parameter estimation from video lacks a common benchmark: existing methods evaluate on non-overlapping synthetic data, the sole real-world dataset is restricted to single-body systems, and no established protocol addresses governing-equation identification.

By Rasul Khanbayov, Mohamed Rayan Barhdadi, Erchin Serpedin, Hasan Kurban
arXiv Machine Learning
1d ago

MiDAS: A Multimodal Data Acquisition System and Dataset for Robot-Assisted Minimally Invasive Surgery

arXiv:2602. 12407v3 Announce Type: replace-cross Abstract: Background: Robot-assisted minimally invasive surgery (RMIS) research increasingly relies on multimodal data, yet access to proprietary robot telemetry remains a major barrier.

By Keshara Weerasinghe (MD), Seyed Hamid Reza Roodabeh (MD), Andrew Hawkins (MD), Zhaomeng Zhang, Zachary Schrader, Homa Alemzadeh