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:2606. 27918v1 Announce Type: cross Abstract: As a prominent symptom of Parkinson's disease (PD), turning impairment is evaluated through parameters such as turning angle, duration, and particularly, the number of steps required to complete a turn, which directly reflects motor dysfunction.
By Qiushuo Cheng, Jingjing Liu, Catherine Morgan, Alan Whone, Majid Mirmehdi
arXiv:2408. 08182v5 Announce Type: replace-cross Abstract: People with Parkinson's Disease (PD) often experience progressively worsening gait, including changes in how they turn around, as the disease progresses.
By Qiushuo Cheng, Catherine Morgan, Arindam Sikdar, Alessandro Masullo, Alan Whone, Majid Mirmehdi
arXiv:2609.22956v1 Announce Type: new
Abstract: Parkinson's disease alters gait and bilateral coordination, but machine-learning performance also depends on how continuous gait signals are represente...
By Md. Sifat, Sania Akter, Akif Islam, Md. Ekramul Hamid
The paper introduces a cross‑modal distillation framework that combines the accuracy of inertial measurement unit (IMU) data with the practicality of video‑based gait analysis to detect freezing of gait (FOG) in Parkinson’s patients. By extracting invariant latent topologies from a pre‑trained kinematic oracle, the method supervises a visual architecture and fuses skeletal graph nodes with continuous spatial pixels to handle severe spatial occlusion during continuous 360° turns. Experiments on a public multimodal dataset show that this approach reduces tracking entropy and achieves precise FOG predictions without requiring wearable sensors.
By Chandan Biswas, Aryan Singh, Anabik Pal
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:2604. 18372v2 Announce Type: replace Abstract: Parkinson's disease (PD) is a chronic neurodegenerative disorder.
By Meheru Zannat
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:2609.22619v1 Announce Type: new
Abstract: Tracking recovery of walking function requires detecting meaningful gait change across rehabilitation sessions, yet objective 3D measurement remains co...
By Nethmi Jayasinghe, Mihir Parashar, Amit Ranjan Trivedi
arXiv:2604.01997v2 Announce Type: replace
Abstract: Gait analysis provides an objective characterization of locomotor function and is widely used to support diagnosis and rehabilitation monitoring ac...
By Elisa Motta, Marta Lorenzini, Clara Mouawad, Alberto Ranavolo, Mariano Serrao, Arash Ajoudani
arXiv:2610.05552v2 Announce Type: replace
Abstract: Three-dimensional gait analysis guides rehabilitation after spinal cord injury but depends on marker-based motion capture and force plates, which f...
By Shreyasvi Natraj, Mathieu Ruepp, Yanke Li, Robert Riener, Inge Eriks-Hoogland, Diego Paez-Granados
The paper investigates what information is captured by egocentric video embeddings from models such as VideoMAEv2, CLIP, DINOv2, and VC-1, finding that the leading principal components largely reflect where the head‑mounted camera is looking. It also explores how to combine these video embeddings with body‑worn inertial sensor data for human activity recognition, showing that propagating inertial predictions over a video‑based graph can significantly improve classification performance (macro‑F1 from 0.648 to 0.852). The study demonstrates that video and inertial modalities play distinct roles—video identifies similar clips within a set, while inertial data labels the activities—yet fusing them directly in a single classifier or adding inertial edges to the graph does not yield better results.
By Kansuke Habano