arXiv Machine Learning

From Observed Viability to Internal Predictive Approximation: A Single-Subject Latent-Space Analysis of Gait Dynamics Under Occlusal Constraint

arXiv:2605. 15862v2 Announce Type: replace Abstract: Understanding adaptive biomechanical systems requires distinguishing observable performance, static multivariate representation, longitudinal displacement, and internal approximation of observed change.

arXiv Machine Learning
Jul 14

From Performance to Representational Adequacy: A Representational Bootstrap Framework for Adaptive Biological Systems

arXiv:2606. 01374v3 Announce Type: replace Abstract: Observable performance is commonly used to characterize biological systems, yet aggregated outputs may remain insufficient for uniquely resolving observational conditions, and richer multivariate representations may retain substantial ambiguity.

By Jacques Raynal, Pierre Slangen, Elsa Raynal, Jacques Margerit
arXiv AI
Jun 9

Quantifying Rodda and Graham Gait Classification from 3D Markerless Kinematics derived from a Single-view Video in a Heterogeneous Pediatric Clinical Cohort

arXiv:2605. 11314v3 Announce Type: replace-cross Abstract: Cerebral Palsy (CP) is a neurological disorder of movement and the most common cause of lifelong physical disability in childhood.

By Lauhitya Reddy, Seth Donahue, Jeremy Bauer, Susan Sienko, Anita Bagley, Joseph Krzak, Maura Eveld, Karen Kruger, Ross Chafetz, Vedant Kulkarni, Hyeokhyen Kwon
arXiv AI
5d ago

Towards Context-Aware Clinical Motion Understanding in Daily Living at Home: Freezing of Gait Detection with Egocentric Vision

arXiv:2608. 13283v1 Announce Type: new Abstract: Understanding motion in daily living requires context beyond kinematics, because similar inertial patterns during activities of daily living (ADLs) can reflect intentional stopping, object interaction, or pathological movement impairment.

By Vayalet Stefanova, Diwas Lamsal, Margot Genbrugge, Maxim Yudayev, Christian Schlenstedt, Moran Gilat, Bart Vanrumste, Benjamin Filtjens
arXiv AI
Jun 8

OGA-AID: Clinician-in-the-loop AI Report Drafting Assistant for Multimodal Observational Gait Analysis in Post-Stroke Rehabilitation

arXiv:2604. 05360v2 Announce Type: replace-cross Abstract: Gait analysis is essential in post-stroke rehabilitation but remains time-intensive and cognitively demanding, especially when clinicians must integrate gait videos and motion-capture data into structured reports.

By Khoi T. N. Nguyen, Nghia D. Nguyen, Hui Yu Koh, Patrick W. H. Kwong, Karen Sui Geok Chua, Ananda Sidarta, Baosheng Yu
arXiv Machine Learning
Jul 30

CalTwin: Towards Calibrated, Shift-Robust Medical World Models via Fisher-Information Regularisation

arXiv:2607. 26752v1 Announce Type: new Abstract: Medical world models aim to learn a latent state of patient or organ physiology and a transition function that forecasts how that state evolves under interventions, supporting downstream tasks from imaging-based diagnosis to digital-twin treatment planning.

By Behraj Khan, Shabir Ahmad, Syed Ahmad Chan Bukhari, Tahir Qasim Syed
Hugging Face Trending Papers
Jul 27

Stress-Testing EEG Foundation Models for Clinical Decoding: Dataset Identity and Targeted Negative Controls

Pretrained EEG foundation models are increasingly proposed for clinical decoding, but their transfer across populations and robustness to negative controls remain unclear. We benchmark six models (LaBraM, EEGMamba, CBraMod, REVE, BENDR, and BIOT) on five clinical tasks across four datasets using frozen linear probes with leave-one-subject-out, subject-grouped, or explicitly identified recording-level splits.