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.
By Rui Wang, Byungwon Min, Suxing Liu
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.
By Jethro Odeyemi, W. J. Zhang
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.
By Mohammad Arif Ul Alam
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.
The paper presents a simulator‑grounded framework, 3DTongueQA, that generates verifiable muscle‑grounded question‑answer pairs from 3D tongue meshes. By mapping 11‑dimensional muscle activations to fixed‑topology tongue meshes using the ArtiSynth Badin finite‑element model, the authors produce over 891,000 QA records per language, demonstrating portability across English and Korean. Experiments show that the dataset supports both structured prediction and natural‑language QA, achieving high accuracy with various decoder architectures and robust performance on unseen anchors.
By Seungho Eum, Unsang Park
The study audits the impact of exercise‑specific joint selection on skeleton‑based correctness classification using 1,057 repetitions from ten REHAB24‑6 subjects. It finds that the manual‑subset kNN gain varies from 0.055 for pooled out‑of‑fold AUROC to 0.020 for equal‑weight within‑person AUROC, with both intervals including zero. Across 1,000 dimension‑matched random maps, 14 match or exceed the manual pooled result, while 145 do so when bilateral structure and trunk inclusion are also matched; RBF‑SVM shows a positive within‑person gain, whereas logistic regression and a random‑convolution comparator show negative gains under that estimand.
By Haotian Chen, Jingkun Yu, Yuning Zhang, Bowen Ye
arXiv:2609.14765v1 Announce Type: cross
Abstract: Hip exoskeletons may improve recovery from unexpected gait perturbations, yet personalizing assistance remains difficult because balance is multidime...
By Yun Chen, Oluwasegun T. Akinniyi, Qiang Zhang
arXiv:2607. 20237v1 Announce Type: new Abstract: Rehabilitation scoring systems are most useful when their outputs can be reviewed and interpreted within clinical workflows.
By Yankai Zheng, Yuhe Liu, Yuxin Ma, Tianci Xue, Jiayuan Tian, Yu Fu, Yuxuan Hu, Jianing Wang, Zichun Xiao, Junya Mu, Shaohui Ma
Radar-based human pose estimation has focused on improving learning algorithms while representing the body as unconstrained keypoint coordinates. We address the underexplored dimension of anatomical fidelity by integrating a full-body skeletal model into a differentiable, end-to-end trainable radar-based pose estimation framework, in which the pose network is supervised through forward kinematics while subject-specific geometry is fitted beforehand.
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.
By Florian Delaplace (LAMHESS, CHU), Elodie Piche (LAMHESS), Fr\'ed\'eric Chorin (IUF, LAMHESS), Raphael Zory (IUF, LAMHESS)