Fall-Risk Classification in Amputees Using Smartphone Sensor Based Features in Turns
Bibliographic record
Abstract
Identifying people at risk of falling can prevent life altering injury. Existing research has demonstrated fall-risk classifier effectiveness in older adults from accelerometer-based data. The amputee population should similarly benefit from these classification techniques; however, validation is still required. 83 individuals with varying levels of lower limb amputation performed a six-minute walk test while wearing an Android smartphone on their posterior belt, with TOHRC Walk Test app to capture accelerometer and gyroscope data. A random forest classifier was applied to feature subsets found using three feature selection techniques. The feature subset with the greatest accuracy (78.3%), sensitivity (62.1%), and Matthews Correlation Coefficient (0.51) was selected by Correlation-based Feature Selection. The peak distinction feature was chosen by all feature selectors. Classification outcomes with this lower extremity amputee group were similar to results from elderly faller classification research. The 62.1% sensitivity and 87.0% specificity would make this approach viable in practice, but further research is needed to improve faller classification results.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".