Evaluating predictability in outdoor mobility: A potential pathway to personalized assistance for people with dementia
Bibliographic record
Abstract
Abstract Background People with dementia (PwD) often become lost, which is commonly attributed to spatial disorientation, one of the earliest symptoms of dementia, particularly of the Alzheimer’s disease type. Spatial disorientation can limit a person's ability to navigate in an outdoor environment. As a result, PwD experience outdoor mobility decline, which, in turn, can have a negative impact on their cognitive functions. Thus, enabling safe outdoor mobility is important for dementia research. Artificial Intelligence (AI) methods in conjunction with Global Positioning System (GPS) data show great potential for supporting the outdoor mobility needs of PwD. The objective of this work is to evaluate the extent to which we can predict future destinations of PwD by learning from their past mobility habits. Method Eight cognitively‐intact older adults (CTL) and seven older adults with dementia completed four weeks of GPS recording. Each participant’s stops and trips were extracted from their trajectories. We determined the predictability of each participant’s mobility patterns using three approaches. First, we assumed each stop is visited with equal probability, thus, disregarding temporal aspects of travel. Next, we built on the previous approach by including stop visitation probability, thus, capturing the heterogeneity of visitations. Finally, to capture the full spatiotemporal characteristics of mobility, we added in‐depth temporal characteristics including the visitation frequency, the order in which the stops were visited, and the time spent at each stop. Result Relying solely on the spatial dimension of mobility yielded no predictive power across the two groups. Adding the heterogeneity of visitation patterns, we observed an increase in the predictability power; PwD displayed a trend toward higher predictability compared to the CTLs, but the student’s t‐test did not reach statistical significance (0.833±0.085vs.0.768±0.029, t(13)=‐2.06, p=0.06; d=‐1.07). Finally, relying on full spatiotemporal characteristics, a 4‐week record of mobility patterns displayed 95% (SD=2%) and 92% (SD=1%) predictability among PwD and CTLs, respectively. This value was significantly higher among PwD, t(13) = ‐3.39, p<.01; d=‐1.75. Conclusion Our findings offer new perspectives on the predictive mobility models based on GPS data and AI that can be used to provide personalized assistance for outdoor navigation of people with dementia.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".