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.
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...
The study evaluates how well a surface‑EMG gesture recogniser trained on intact‑limb data transfers to transradial amputees. Zero‑shot transfer fails; the model needs a few labelled repetitions from the new user to outperform a per‑user classifier, achieving a macro‑F1 of 0.779 versus 0.589. Training on a larger pool of intact subjects, or combining intact and amputee data, yields the best cross‑population performance.
The paper evaluates six onboarding strategies for federated wearable models on five datasets using a leakage‑controlled protocol that fixes source checkpoints and separates calibration from evaluation. Results show that while average accuracy is high, person‑level performance can drop significantly, with some methods causing negative transfer for certain users. The study highlights that mean accuracy alone is insufficient and provides an auditable benchmark and failure map for future development.
The paper presents a training‑free method for detecting which holds a climber uses in sport climbing videos by leveraging a frozen foundation pose model (Sapiens) that provides fingertip and toe keypoints. Using a simple proximity test, mutual exclusion, and a temporal‑persistence rule, the approach achieves high F_1 scores (up to 90.2%) on the Way Up dataset without any climbing‑specific training, outperforming repurposed pose pipelines. The resulting automatic predictions enable accurate coaching statistics, such as climb time and pace, with Pearson correlations of 1.00 and 0.94 respectively.
arXiv:2607. 20237v1 Announce Type: new Abstract: Rehabilitation scoring systems are most useful when their outputs can be reviewed and interpreted within clinical workflows.
The study introduces a framework that reconciles minute‑resolution athlete monitoring data with injury labels that are only available at the session level. By creating fixed‑time landmarks (10, 20, 30, 40, 50, and 60 minutes) and generating a single representation per athlete‑session up to each landmark, the authors evaluate several machine‑learning models and data‑augmentation strategies on elite women’s football data. Results show that discrimination varies across landmarks, with TabPFN outperforming Logistic Regression at later landmarks but not consistently beating Random Forest, and that synthetic augmentation offers benefits only in specific conditions.
arXiv:2609.31461v1 Announce Type: new Abstract: Background: Large volumes of unlabeled knee MRI scans are available across repositories but remain insufficiently leveraged. We developed KneePreM, a k...
The paper investigates 12‑class body‑only emotion recognition from skeleton motion using a leave‑performer‑out evaluation, where chance accuracy is 8.3% and a reproduced STGCN++ baseline scores 25.73% Macro‑F1. By ensembling eleven models with orthogonal error modes, the authors achieve 36.80% Macro‑F1, a 43% relative improvement over the baseline. They also introduce a tested explanation suite that demonstrates the ensemble’s decisions rely on motion‑grounded body‑region evidence, aligning strongly with Laban Movement Analysis attributes rather than classical kinematics, while showing diffuse temporal saliency.
arXiv:2608.29336v1 Announce Type: new Abstract: FISICA is a body-assessment and recommendation service running in production. One standing session with two photographs returns foot-loading measures,...
arXiv:2608. 12145v1 Announce Type: cross Abstract: Autonomous rehabilitation systems must not only recognize human motion but also provide structured feedback to support users without continuous therapist supervision.
The Action Research Arm Test (ARAT) is a widely-used upper limb outcome measure in neurorehabilitation, but its ordinal scoring is subjective and suffers from limited sensitivity and specificity. We evaluated whether artificial-intelligence (AI)-based markerless motion capture (MMC), embedded into ARAT assessments during clinical routine, accurately reconstructs upper limb movement and yields valid, objective kinematic metrics carrying clinically meaningful information beyond the ordinal score.
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.