Gait-Guided Adaptive Interfaces: Managing Cognitive Load In Older Users
Bibliographic record
Abstract
Mobile computing provides new ways to interact with technology; applications such as navigation, social facilitation, and augmented reality are used while walking. We introduce Gait-Guided Adaptive Interfaces (GGAIs) as a way to manage cognitive load in dual-task conditions (walking while using a device). Gait markers that can be suitably assessed using smartphone sensors (decreased gait speed, increased variability) have been shown to be indicative of cognitive load in older adults. Motor-cognitive interference is a more significant issue as we age, gait becomes less automatic, and the risk of falls under distraction increases. Apps with GGAIs measure changes in gait to infer load and then adapt the way that the App interacts with the user accordingly. We validate this approach using a simple Go/No-Go task, and then show how gait responds to changes in task complexity. We conclude with a discussion of how GGAIs may be used by developers to improve the usability of apps for older users.
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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 | 0.005 |
| 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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".