Machine Learning-Based Stroke Patient Rehabilitation Stage Classification Using Kinect Data
Bibliographic record
Abstract
Everyone aspires to live a healthy life, but many will inevitably experience some form of disease, illness, or accident that results in disability at some point. Rehabilitation plays a crucial role in helping individuals recover from these disabilities and return to their daily activities. Traditional rehabilitation methods are often expensive, are inefficient, and lead to slow progress for patients. However, in this era of technology, various sensor-based automatic rehabilitation is also possible. A Kinect sensor is a skeletal tracking device that captures human motions and gestures. It can provide feedback to the users, allowing them to better understand their progress and adjust their movements accordingly. In this study, stroke-based rehabilitation is presented along with the Toronto Rehab Stroke Pose Dataset (TRSP). Pre-processing of the raw dataset was performed using various features, and several state-of-the-art classifiers were applied to evaluate the data provided by the Kinect sensor. Among the various classifiers, eXtreme Gradient Boosing (XGB) attained the maximum accuracy of 92% for the TRSP dataset. Furthermore, hyperparameters of the XGB have been optimized using a metaheuristic gray wolf optimizer for better performance.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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".