Sensor System to Determine Force and Direction for Push Handles on Patient Transport System
Bibliographic record
Abstract
This Thesis presents the theory, development, and performance analysis of a novel force sensor system for the Able Innovations ALTATM patient transfer system. ALTATM is an alternative to slings, but it is heavy, and healthcare workers will require power assistance to move and position the system. Handles are provided for staff and the proposed sensor system measures the applied force magnitude and direction applied to these handles for use with motion assist motors. Load cell sensors, strain gauge sensors, proximity sensors and force sensitive resisters are considered and a solution using strain gauges is proposed that allows for the measurement of direction of force with minimal error without requiring detailed calibration of the sensors. Machine Learning comparing Logistic Regression, Linear Discriminant Analysis, Support Vector Machine, Decision Tree and Random Forest is tested to predict the angle and magnitude of the force with Decision Tree providing an accuracy of over 99%.
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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) | 0.001 | 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".