A Neuro-Fuzzy-Based Sensing Approach for the Classification of Emulated Postural Instability
Bibliographic record
Abstract
Assistive Technology helps to assess the daily living of frail people and may have a strategic role to detect and prevent falls. In this paper, the task of classifying different classes of postural sway behaviors has been addressed by developing a Neuro-Fuzzy inference approach that is robust against noise. The proposed approach classifies four different postural behaviors namely Stable Standing, Antero-Posterior, Medio-Lateral and Unstable. The strategy exploits data generated by a wearable sensor node, to be positioned on the user chest. A dedicated experimental set-up has been realized to emulate the postural dynamics and generate the dataset. Two novel indices to assess the robustness of the system have been proposed. The first index is a measure of residuals between the predicted and the expected postural status, which equally weights estimations with respect to expected classes. The second metric is a reliability index, which allows for assessing the degree of trust of each estimation performed by the Neuro-Fuzzy inference. Results obtained demonstrate the suitability of the proposed methodology, showing a capability of almost 100% to correctly classify patterns among different allowed classes, with reliability indexes of 97.56% and 98.50% for the training and test patterns, respectively. Also, robustness of the Neuro-Fuzzy classification algorithm against noisy data has been demonstrated.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".