The Flow has not summarised this story yet — read it at arXiv Computer Vision.
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.
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...
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.
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.
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.
RevalExo is a new benchmark for locomotion mode recognition that focuses on functional daily activities performed by older adults and clinical cohorts. It includes 27 participants from three groups—healthy older adults, stroke survivors, and older adults with probable sarcopenia—recorded with lower-body IMUs and, for a subset, synchronized egocentric video. The dataset offers 10.1 hours of frame‑level annotations across 11 locomotion modes, and the authors evaluate unimodal, multimodal, cross‑population, and cross‑modal recognition challenges, finding that sensor fusion improves performance but transitions and generalization remain difficult.