MULTISENSOR APPROACHES FOR COMPREHENSIVE MEASUREMENT OF REAL-WORLD MOBILITY IN OLDER ADULTS
Bibliographic record
Abstract
Abstract Despite the potential for collecting precise and accurate mobility data over extended periods of time, the use of body-worn sensors as an approach to mobility monitoring is at a crossroads. The broad use of consumer smartwatches has led to a focus on single sensor approaches with simple to digest outcomes (e.g., step counts) however, this approach can limit our ability to capture details about how, where and when people are moving which are necessary to guide intervention. In this talk, we will provide evidence for the feasibility and value of a multi-sensor approach to mobility measurement that optimizes a’benefit-to-burden’ ratio for older adult participants. Specifically, we will provide examples to highlight how the use of multiple sensors can improve fidelity, reduce uncertainty, and provide important context to the data in order to maximize our understanding of real-life mobility. We will also share lessons learned from several multisensor-based studies of mobility across a spectrum of older adults (healthy, neurodegeneration, exceptional cognitive aging) that have contributed to successful implementation of a multi-sensor approach. The principles presented will inform future work aimed at developing validated standards for multi-sensor derived digital mobility outcomes that would provide comprehensive information on mobility for risk identification, early intervention, and monitoring of disease progression and treatment efficacy.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".