Classification of pathological and healthy individuals for computer-aided physical rehabilitation
Bibliographic record
Abstract
The role of computer-aided therapy in physical rehabilitation has significantly grown in recent years. In order to optimize the therapeutic actions based on the patient disease and improve the interaction between the patient and telemedical software, it is important to differentiate the healthy individuals and patients following different chronic diseases or musculoskeletal disorders. In this paper, we propose a deep learning method to classify the trajectory patterns in physical rehabilitation as healthy or pathological. Motion sequences containing joint positions and joint angles are transformed into an image representation, which enables the training of a classification model using a deep 2D Convolutional Neural Network (CNN) to infer a health state or a symptom. Our approach was evaluated on two publicly available datasets, the KIMORE [1] dataset for complete body skeleton sequences and the Toronto Rehab Stroke Posture (TRSP) [2] dataset containing upper body skeleton sequences. The current method effectively classifies the healthy and pathological movements and achieves 78.57% of accuracy on KIMORE dataset and 86.20% of accuracy on TRSP data.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".