Evaluating Postural Sway in the Elderly Using Inertial Measurement Units: A Study on Center of Mass Measurements via Accelerometers and Gyroscopes
Bibliographic record
Abstract
Background: Assessment of center of mass (COM) changes during static stance control has practical implications in clinical settings, notably among older adults. Recently portable and wearable devices, including accelerometers and gyroscopes, have emerged as a promising alternative to traditional clinical and laboratory assessments. The objectives of the study were to evaluate COM postural sway parameters derived from accelerometer and gyroscope data during static balance tasks with varying bases of support in healthy elderly individuals, and to examine the correlation and agreement between accelerometer-based and gyroscope-based parameters in postural sway assessment. Methods: One hundred and fourteen healthy elderly individuals who had not experienced falls within the preceding 6 months and were confirmed to have no risk of falling as determined by the timed up and go test, were included in this study. They were evaluated for postural sway while standing, using the sensor securely with a belt attached to the L5 vertebra. The four-stage balance test, including standing in a double stance (SO), semi-tandem stance (STO), tandem stance (TO), and single-leg stance (SL), was employed to assess each participant's ability to maintain balance under increasingly challenging standing positions on a stable surface. Results: The study demonstrated that COM posture sway increased with a demanding position and a decreasing base of support. Spearman's rho correlation coefficients from the anteroposterior and mediolateral planes exhibited strong correlation (0.75 - 0.9). Moderate reliability was observed for both the accelerometer and gyroscope parameters in both planes (intraclass correlation coefficient: 0.5 and 0.75). Conclusions: Accelerometry and gyroscopes provide objective quantification of balance that have the potential to be utilized in conjunction with clinical tests to effectively evaluate the risk of falling.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".