Automated Detection of Multidirectional Compensatory Balance Reactions: A Step Towards Tracking Naturally Occurring Near Falls
Bibliographic record
Abstract
Falls are the leading cause of fatal and non-fatal injuries among seniors with serious and costly consequences. Laboratory evidence supports the view that impaired ability to execute compensatory balance reactions (CBRs) or near-falls is linked to an increased risk of falling. Therefore, as an alternative to the commonly used fall risk assessment methods examining spatial-temporal parameters of gait, this study focuses on the development of machine learning-based models to detect multidirectional CBRs using wearable inertial measurement units (IMUs). Random forest models were developed based upon the data captured by five wearable IMUs to 1) detect CBRs during normal gait, and 2) identify the type of CBR (eight different classes). A perturbation treadmill (PT) was employed to systematically elicit CBRs (i.e. PT-CBRs) during walking in different directions (e.g slip-like, trip-like, and medio-lateral) and amplitudes (e.g., low-, high-amplitude). We hypothesized that these PT-CBRs could simulate naturally-occurring CBRs (N-CBRs). Proof-of-concept testing in 9 young, healthy adults demonstrated accuracies of 96.60% and 80.64% for the PT-CBR detection and type identification models, respectively. Performance of the detection model was tested against a published dataset (IMUFD) simulating N-CBRs, including the most common types observed in older adults in long-term care facilities, which achieved sensitivity of 100%, but poor specificity. Adding normal gait data from IMUFD for training improved specificity, indicating treadmill walking alone is insufficient exemplar data. Perturbation treadmill combined with overground walking data is a suitable paradigm to collect training datasets of involuntary CBR events. These findings suggest that accurate detection of naturally-occurring CBRs is feasible, and supports further investigation of implementing a wearable sensor system to track naturally-occurring CBRs as a novel means of fall risk assessment.
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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 | 0.003 |
| 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 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".