Machine learning based detection of compensatory balance responses to lateral perturbation using wearable sensors
Bibliographic record
Abstract
Loss of balance is prevalent in the older population and also in people who have mobility impairment. The primary aim of the present paper is to develop an efficient classifier to automatically distinguish compensatory balance responses (or near-falls) from regular stepping patterns. In this study, 5 young, healthy subjects were perturbed by lateral pushes while walking and the compensatory reactions were recorded by three wearable inertial measurement units (IMUs). Time domain features of these signals were extracted and reduced, using different dimension reduction methods, i.e., PCA, SPCA and KSPCA. The performance of k-nearest neighbor (k-NN) and support vector machines (SVMs) classification methods for detection of compensatory balance responses is investigated. The results of this study advances wearable measurement methods to accurately and reliably monitor gait balance control behavior in at-home settings (unsupervised conditions), over long periods of time (i.e., weeks, months). Building on the current study, subsequent research will examine ambulatory data to identify balance recovery processes for clinical assessment of fall risk.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".