SPLIGN: Smart-belt Posture Monitoring System Based on AI-algorithms for Sitting Persons
Bibliographic record
Abstract
The back pain is the main common health problems on this decade because of the sitting for a long time tilted on the computer for working, studying or playing. The back problems are widely spread for different ages from young to old people. Last medical research demonstrates that sitting with the posture align can prevent and remedy many spine problems. In this paper, we propose ’SPLIGN’ which a posture monitoring system designed to help maintaining the good posture during sitting. The SPLIGN is a smart belt equipped with inertial sensors. A mobile and web applications are developed for monitoring and remind the user to correct posture. The proposed system is based on a detailed study of the machine learning algorithms in order to choose the best accurate algorithm for posture prediction. The main studied algorithms are Convolutional neural network (CNN), The K-nearest Neighbors (KNN), Support-vector machines (SVM), Decision tree classification, Random forest, Naive Bayes Classifier and Boosting algorithm. The test results demonstrate that The Random Forest algorithm has the best accuracy 99.67% compared to the other algorithms with appropriate processing time 67.7 ms for real time posture monitoring system.
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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 | 0.001 | 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".