Fusing Camera and Electromyography Data for Enhanced Range of Motion Assessment
Bibliographic record
Abstract
Accurate and automated Range of Motion (ROM) assessment is essential for rehabilitation, physical therapy, and post-surgical recovery. Traditional manual goniometer-based evaluations suffer from subjectivity, inter-rater variability, and reliance on trained professionals. Although RGB-based computer vision enables markerless ROM estimation, it remains susceptible to occlusions, pose estimation errors, and difficulties detecting subtle joint movements. Furthermore, vision alone cannot capture neuromuscular activation, which is crucial for understanding joint dynamics. To address these challenges, we propose a multi-modal deep learning framework that integrates RGB-based motion tracking with electromyography (EMG) signals. EMG provides neuromuscular activation data, enhancing the robustness against visual occlusions and improving sensitivity to subtle joint displacements. Our method employs an Hourglass-based convolutional neural network (CNN) for spatial feature extraction and a gated recurrent unit (GRU)-based model for temporal EMG processing. To further enhance performance, we introduce feature-level and modality-level attention modules, dynamically emphasizing the most informative features and modality contributions. Experimental results demonstrate that our proposed model achieves an overall RMSE of 2.55, and improvements via the feature and modality attention mechanisms, respectively. Moreover, the fully fused RGB-EMG model outperforms RGB-only approaches, particularly in accurately predicting subtle ROM movements.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".