Fall Risk Assessment in Active Elderly Through the Use of Inertial Measurement Units: Determining the Right Postural Balance Variables and Sensor Locations
Bibliographic record
Abstract
Falls among the elderly have been a significant public health challenge, with severe consequences for individuals and healthcare systems. Traditional balance assessment methods often lack ecological validity, necessitating more comprehensive and adaptable evaluation techniques. This research explores the use of inertial measurement units to assess postural balance in relation to the Berg Balance Scale outcomes. We recruited 14 participants from diverse age groups and health backgrounds, who performed 14 simulated tasks while wearing inertial measurement units on the head, torso, and lower back. Our study introduced a novel metric, i.e., the volume that envelops the 3-dimensional accelerations, calculated as the convex hull space, and used this metric along with others defined in previous studies. Through logistic regression, we demonstrated significant associations between various movement characteristics and the instances of balance loss. In particular, greater movement volume at the lower back (p = 0.021) was associated with better balance, while root-mean-square lower back angular velocity (p = 0.004) correlated with poorer balance. This study revealed that sensor location and task type (static vs. dynamic) significantly influenced the coefficients of the logistic regression model, highlighting the complex nature of balance assessment. These findings underscore the potential of IMUs in providing detailed objective balance assessments in the elderly by identifying specific movement patterns associated with balance impairment across various contexts. This knowledge can guide the development of targeted interventions and strategies for fall prevention, potentially improving the quality of life for older adults.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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 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".