arXiv AI By Md. Sifat, Sania Akter, Akif Islam, Md. Ekramul Hamid

A Compact Stance-Indexed Anterior-Posterior COP Representation for Parkinson's Disease Classification from Plantar VGRF

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arXiv AI
Aug 24

Aggregate, Don't Adapt: Subject-Level Posterior Aggregation and Transductive Calibration for Cross-Site Parkinsonian Gait Severity

The paper reports the winning solution to the MoCha 2026 Parkinsonian Gait Benchmark, achieving a macro‑F1 score of 0.6945 on unseen clinical sites. The approach relies on a frozen public motion encoder followed by a single 4×512 linear layer, and gains are largely attributed to three key steps: exact replication of the benchmark’s head recipe, averaging per‑walk posteriors at the subject level, and a label‑free transductive calibration of feature means and decision thresholds. Extensive ablation studies show that fine‑tuning the encoder or using alternative encoders does not improve performance, and the subject‑level aggregation is identified as the primary contributor to the top score.

By Junlong Shen
arXiv Machine Learning
Jul 14

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.

By Jacques Raynal, Pierre Slangen, Elsa Raynal, Jacques Margerit
arXiv Machine Learning
Sep 14

3D Digital Twin Visualization of Multiclass GRF-Based Gait Disorder Classification

The paper presents a new framework that classifies healthy gait and multiple musculoskeletal impairment groups using bilateral ground reaction force (GRF) and center-of-pressure (COP) signals. After normalizing and standardizing the signals, the model achieved 99.00% validation accuracy and 90.07% test accuracy with a session-level split. The approach includes class‑specific ε‑LRP explainability and synchronizes processed GRF signals with model predictions in a Blender‑based 3D visualization for sample‑level inspection.

By Nayoung Son, Minwoo Shin