Towards an AI-Based In-Bed Posture Detection System for Pressure Injury Prevention
Bibliographic record
Abstract
Pressure injuries (PIs) are common wounds among patients with decreased mobility who are unable to periodically redistribute their body weight. The most common technique to prevent PI development is through frequent repositioning, often requiring support from caregivers, which can be a costly and laborious task. Therefore, this paper investigates the use of a pressure sensitive sheet to automatically capture in-bed body postures to prevent PI development. Five Neural Networks were evaluated to classify 10 sub-postures using pressure distribution images. Two techniques were explored: directly classifying all 10 postures, and a hierarchical architecture. Although the hierarchical architecture with the ShuffleNet algorithm achieved the highest F1-Scores of 99.75% ± 1.43% for holdout (20% test set) and 93.53% ± 7.37% for Leave-One-Subject-Out (LOSO) cross-validation, direct classification provides more stable results. These results suggest that this approach has promising potential to detect common sub-postures and could be used to remind caregivers to facilitate timely repositioning, thereby preventing PI development.
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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 | 0.000 | 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".