Autonomous Virtual Cognitive Assessment via NLP and Hand Gesture Recognition
Bibliographic record
Abstract
Fast-paced and ever-growing advances in Signal Processing and Machine Learning (ML) models have initiated works on autonomous medical monitoring/screening tasks to assess patients' cognitive state. In conventional cognitive assessment systems, a physician evaluates the mental abilities of the brain by rating the patient's numerical, verbal, and logical responses. Development of an autonomous cognitive assessment system that replaces the physician is a critically challenging task. As a first step towards achieving this objective, in this paper an Automated Virtual Cognitive Assessment (AVCA) framework is proposed that integrates Natural Language Processing (NLP) and hand gesture recognition techniques. The proposed AVCA framework provides individual scores in the seven major cognitive domains, i.e., orientation, attention, language, contractual ability, memory, calculation, and reasoning. More specifically, audio and video signals are fed to the framework in a real-time fashion, where semantic and synthetic analysis are performed using NLP techniques and Deep Neural Network (DNN) models. The AVCA's real-time video processing engines interpret patients' video signals to monitor their hand gestures and enable easier interaction. Initial simulation results corroborate effectiveness of the proposed AVCA framework.
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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 | 0.002 |
| 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 | 0.001 |
| 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 | 0.001 |
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".