Using a wearable camera to support everyday memory following brain injury: a single-case study
Bibliographic record
Abstract
Abstract Background: Wearable cameras have been shown to improve memory in people with hippocampal amnesia and Alzheimer's disease. It is not known whether this benefit extends to people with amnesia of complex or uncertain origin. Method: This case study examined the effect of wearable camera use on memory and occupational performance in a patient with memory loss and complex mental health problems following a severe neurological incident. With the help of his occupational therapist (OT), Mr A used a wearable camera to record a series of eight personally significant events over a 6-week period. During visits from his OT, Mr A was asked to report what he could remember about the events, both before (baseline) and during the review of time-lapsed photographs captured automatically by the camera. Results: The results showed striking improvements in recall while reviewing the photographs, relative to baseline recall, but the additional details recalled during review did not appear to be maintained at later tests, after several days. Across the study period, there were moderate increases in occupational performance, measured using the Canadian Occupational Performance Measure. However, after the study period ended, Mr A ceased to use the wearable camera due to technological difficulty. Conclusion: There was a clear benefit of wearable camera use, but the real-world impact of the technology was limited by the complexity of the system. The results of the study are discussed alongside novel clinical insights and suggestions for developing wearable camera support systems that can be used independently by people with memory problems.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".