The Flow has not summarised this story yet — read it at arXiv Computer Vision.
arXiv:2607. 16631v1 Announce Type: new Abstract: Translating unstructured clinical prescriptions into patient-specific foot orthoses (FOs) is hindered by a semantic-physical misalignment: high-level clinical intent is not mapped deterministically onto the 3D geometric parameters of the orthosis, and existing design workflows remain dependent on manual expertise with no instantaneous biomechanical validation.
Quantitative joint angles are rarely available in routine care because the tools are slow, costly, or confined to a laboratory. We show that clinical joint angles can be read directly from the per-segment rotation matrices a parametric body model already produces, with no inverse-kinematics or musculoskeletal-model fitting step.
arXiv:2607. 27565v1 Announce Type: new Abstract: Pattern-recognition control promises a myoelectric prosthesis that responds to many intended gestures rather than one or two, but the promise has stayed in the laboratory.
The study investigates how well a single consumer earbud IMU can estimate 3D body pose and whether adding foot IMUs improves accuracy. Using a multimodal capture pipeline with RGB‑D video, an AirPods head IMU, and Striv insole IMUs, the authors benchmark pose estimation across various motions and train recurrent models (IMUPoser and MobilePoser). Results show that a head IMU alone achieves 79.0 mm rigid‑MPJPE and 0.809 macro‑F1 for foot contact, while adding foot IMUs does not significantly improve pose and can even degrade performance due to insole orientation errors.
The study presents a real‑time musculoskeletal surrogate for children with cerebral palsy, built from OpenSim parameters, joint kinematics, and muscle capacities. Using leave‑one‑subject‑out validation on nine pediatric gait recordings, the surrogate reproduces musculotendon lengths with high accuracy (R² ≈ 0.92–0.95, nRMSE < 8%) and achieves sub‑millisecond inference times, well below the 100 ms interactive‑rehabilitation target. A Monte Carlo credibility pilot reveals that small variations in anthropometry and muscle capacity lead to overconfident 90 % prediction intervals, highlighting the need for improved force modeling and uncertainty quantification.
Pattern-recognition control promises a myoelectric prosthesis that responds to many intended gestures rather than one or two, but the promise has stayed in the laboratory. A recogniser trained on one person rarely transfers to the next, and useful performance usually demands a fresh round of labelled calibration from the end user.