Machine learning-based risk of fall estimation using insole with force sensors while performing a sequence of activities in the TUG test
Bibliographic record
Abstract
Several methods combining biomedical and computer-based approaches have been used to address the risk of falls among the elderly using instrumented insoles. Machine-learning techniques in gait analysis has proven to be a promising solution when using instrumented insoles. However, no study has investigated the risk of falls associated with a sequence of activities. Indeed, it can be observed that an important amount of energy is required by individuals preparing to get out of bed or toilet. The main goal of this work is to detect and associate different risk levels by analyzing the sit-to-start-of-walk (STSOW) sequence. Data were acquired during a Timed Up and Go test using an instrumented insole. The proposed approach compares six types of classifiers to the STSOW sequence signals. Then, a recursive clustering approach based on statistical features and the Kruskal Wallis test was implemented to define different levels of risk. The results show the capacity of the proposed approach to associate different risk levels of falls to an STSOW sequence. The accuracies of the classifiers ranged from 69% to 95.2%, and the best accuracy was achieved using both decision tree and ensemble classifiers. For the sit-to-start of the walk sequence identification phase, the best accuracy was achieved using the support vector machine model.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 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) | 0.001 | 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".