Design and Implementation of a Wearable Device for Prosopagnosia Rehabilitation
Bibliographic record
Abstract
This study introduces a wearable facial recognition system for face blindness, or prosopagnosia, rehabilitation. Prosopagnosia is the inability to recognize familiar faces, which affects 2.5% of the world population (148 million people). The design and implementation of a facial recognition system tailored to patients with prosopagnosia is a priority in the field of clinical neuroscience. The goal of this study is to demonstrate the feasibility of implementing a wearable stand-alone (not connected to a PC or a smartphone) system-on-chip (SoC) that performs facial recognition and could be used to assist individuals affected by prosopagnosia. This system is designed as an autonomous embedded platform built on eyewear with SoC and a custom designed circuit board. The implementation is based on the open source computer vision image processing algorithms embedded within a compact-scale processor. The advantages of the device are its lightness, compactness, single independent image processing capability and long operational time. The system performs real-time facial recognition and informs the user of the results by displaying the name of the recognized person.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".