Automatic annotation of head movement among elderly people susceptible to Alzheimer's disease
Bibliographic record
Abstract
Abstract Background Several researchers have revealed that sensory and motor changes can predate the cognitive symptoms of Alzheimer's disease (AD) by many years and may signify increased risk of developing AD. Therefore, studying the non‐verbal communication among the elderly susceptible to AD can contribute to a better understanding of their daily needs. Among these non‐verbal communications, we can mention head gestures, hand gestures, etc. This research discusses a new system model of nonverbal language annotation associated to hand gestures among elderly people susceptible to AD. Method The proposed approach aims at establishing a longitudinal study based on the use of recurrent neural network as a deep learning technique. We propose an interdisciplinary approach for automatic annotation of hand phases (rest position, preparation, hold, retraction), defined in many existing research works. To perform the classification, we rely on a ground truth, initiated by experts, known as CorpAGEst corpus. The latter is a collect of spontaneous conversations between interviewers and elderly people, which is used to train and test our model Result Experimentations show promising results, where the proposed approach succeeded in classifying the hand gesture phases with an 87.5% of precision and recall. Conclusion The proposed process focused on the automatic annotation of hand phases, reducing the cost of manual annotations and establishing a back and forth dialog between computer science communities and researchers in AD. The principal impact of this work is to contribute in establishing stronger gesture recognition techniques adapted to the aging population, giving the community of researchers tools to explore specificities in an automated fashion.
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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 | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".