The Use of Machine Learning Models in Human Body Recognition for Hospital Caregivers with Applications to Turning Immobile Patients
Bibliographic record
Abstract
The application of computer vision techniques in medical technology have resulted in the development of programs that aim to improve the efficiency of hospital processes and workflows. This article entails the development of one such program whose objective to help hospital caregivers in turning their patients to the correct position on their beds. Two particular techniques were used by the researchers to achieve this goal: human recognition to recognize the position of the patient on the bed, and object detection to determine the position of any external factors such as pillows. The researchers developed the application in Python using open-source libraries such as OpenCV and MediaPipe Pose, which was used in tandem with a Raspberry Pi and a mounted camera. TensorFlow Lite, an industry-standard machine learning tool, was used to train the machine learning model for pose classification. Results of the research show that the application is able to determine the correctness of each step of turning the patients to a reasonable degree of accuracy. Further training of the model suggests that this accuracy will increase with each round of subsequent training data. In the future, the researchers hope to supplement the application with additional functionality in the future to better cater to the needs of hospital caregivers.
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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 | 0.001 |
| 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".