Machine Learning to Determine Handle Force and Direction Using Strain Gauge Measurements
Bibliographic record
Abstract
This paper introduces the theory and design for a push handle strain gauge system using machine learning to evaluate force and direction. The Able Innovations ALTA ™ patient transfer system is a new alternative to sling transfers, but the new system is heavy, and healthcare workers will require power assistance to move the system through the facility and to position the transfer system. Handles on the system provide the expected push bars that staff currently use on gurneys and the proposed sensor system measures the applied force magnitude and direction applied to these handles. Machine Learning is used to analyze sensors measurement to predict the angle and magnitude of the force. The results show that the strain gauge sensor provides a linear relationship for applied forces without requiring detailed calibration of each of the sensors. Machine Learning was used to address asymmetry in the mechanical design so that forward/backward and lateral forces could be combined into a combined force magnitude and direction. The results are presented for forces in 8 different directions with 2 gasket materials, 2 handle mount methods and an applied force range of 0 to 3 kg (typical for expected human effort). The best performance machine learning method is the decision tree which predicts the direction of force with an accuracy of99.1 % and force magnitude with an accuracy is 99.8%.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 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".