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.
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.
arXiv:2608. 02408v1 Announce Type: new Abstract: Accurate gait analysis in Parkinson's disease (PD) typically relies on laboratory-based systems to capture biomechanical data, such as ground reaction forces (GRFs).
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...
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.
arXiv:2507. 21018v2 Announce Type: replace-cross Abstract: Automated assessment of human motion plays a vital role in rehabilitation, enabling objective evaluation of patient performance and progress.
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.
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.
arXiv:2609.08038v2 Announce Type: cross Abstract: Smart healthcare monitoring systems require precise action recognition to ensure well-being and timely intervention in critical situations such as fa...
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.
This scoping review examined 117 studies on video-based markerless motion capture, most published from 2024 onward and focused on healthy adults walking in laboratories. The studies identified five main pipeline architectures, but most reported only raw joint angles without biomechanical refinement, achieving sagittal lower‑limb agreement of about 5–6°, which falls short of clinical acceptability. Validation of out‑of‑plane kinematics, kinetics, and performance in older or pathological populations was rare, and emerging computer‑vision techniques such as foundation‑model mesh recovery and differentiable inverse kinematics were largely absent from validated work.
The paper introduces REHAB26-ViewAngles, a dataset of correct and incorrect rehabilitation exercise videos captured from many camera angles, and a new separability metric to evaluate pose‑estimation algorithms. It evaluates single‑camera 2D and 3D pose estimation and four multi‑camera approaches, showing that an optimally placed 2D camera can boost separability by 16.9% over a frontal view and that combining two views can improve accuracy by up to 13.1%. These findings provide concrete guidance for setting up camera systems in home and clinical rehabilitation monitoring.
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. Egocentric vision provides task-related context that may help disambiguate these cases.