Decision Tree Machine Learning to Determine Direction of Steering Force for Hospital Bed Push Handle System
Bibliographic record
Abstract
This work demonstrates the use of Decision Tree and Random Forest machine learning to determine the direction of desired movement from the forces applied to two push-handles on a novel patient transfer system the Able Innovations ALTA™ patient transfer system provides a replacement transfer method for residents in care that cannot transfer on their own that have limited mobility. This new system is significantly heavier that a hospital gurney because of the weight of the mechatronics and healthcare professionals will need power assist to transport and position the system. In this work, two loadcell sensor-based prototypes have been used to measure the input forces and direction applied by a user to two handles. These two prototypes have been placed on a hospital bed to simulate the two handles on the right and left provided to hospital staff to push the system. Machine learning is used to analyze sensor measurements from each handle to predict the movement intent. The results are presented for test pushes in 6 directions that include forward and reverse in each of straight, left, and right turns. Two models of Machine Learning (decision tree classifier and random forest classifier) have been used for 8 and 14 features and are shown to be able to predict the direction of push with high accuracy. The accuracy for 8 features using decision tree and random forest has been measured 93.5% and 97.0% respectively.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".