Reconstruction of body motion during self-reported losses of balance in community-dwelling older adults
Bibliographic record
Abstract
Older adults experience slips, trips, stumbles, and other losses of balance (LOBs). LOBs are more common than falls and are closely linked to falls and fall-injuries. Data about real-world LOBs is limited, particularly information quantifying the prevalence, frequency, and intrinsic and extrinsic circumstances in which they occur. This paper describes a new method to identify and analyze LOBs through long-term recording of community-dwelling older adults. The approach uses wearable inertial measurement units (IMUs) on the feet, trunk and one wrist, together with a voice recorder for immediate, time-stamped self-reporting of the type, context and description of LOBs. Following identification of an LOB in the voice recording, concurrent IMU data is used to estimate foot paths and body motions, and to create body animations to analyze the event. In this pilot study, three older adults performed a long-term monitoring study, with four weeks recording LOBs by voice and two concurrent weeks wearing IMUs. This report presents a series of LOB cases to illustrate the proposed method, and how it can contribute to interpretation of the causes and contexts of the LOBs. The context and timing information from the voice records was critical to the process of finding and analyzing LOB events within the voluminous sensor data record, and included much greater detail, specificity, and nuance than past diary or smartphone reporting.
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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 | 0.001 |
| 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".