Feasibility for Clinical Physical Mobility Measurement using Hearing Aid Accelerometers
Bibliographic record
Abstract
Hearing Aids (HA) present a new wearable sensor platform as they provide an important function to the wearer (hearing assistance) as well as a location for other sensors. The inclusion of accelerometers could enable the measurement of mobility activity such as walking or standing up/sitting down. In this paper a method to assess three standard clinical mobility measures: Five times Sit to Stand (5xSTS), Timed Up and Go (TUG) and 2-minute walk is proposed and assessed for data collected in a pilot study of community living older adults and laboratory subjects. These tests combine walking with stand-up/sit-down and turning actions. The report shows that the proposed algorithm to measure the time to complete the 5xSTS and TUG provides results that match the standard clinical measures using a stopwatch. The report also shows that the proposed step detection algorithm provides a step count for the 2-minute walk. In all cases the algorithm error is well below the minimal clinically important difference for these assessments. The result is a potential for these important assessments that are predictive of fall risk to be completed for community living adults through their hearing aids as they go about their daily activities enabling more frequent measures than possible through clinical visits alone. More timely information on functional changes can support early intervention to mitigate deterioration or prevent falls.
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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 | 0.007 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".